literature review on gestational diabetes

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Gestational diabetes mellitus—recent literature review.

1. Introduction

2. aim of the study, 3. material and methods, 4. results and discussion, 4.1. epidemiology, 4.2. gdm risk factors, 4.3. diagnosing gdm, 4.4. pathogenesis of carbohydrate metabolism disorders in pregnancy, 4.4.1. insulin resistance, 4.4.2. β-cell dysfunction, 4.4.3. other factors, 4.5. covid-19 pandemic and gdm, 4.6. treatment of gestational diabetes, 4.6.1. nutritional treatment, 4.6.2. exercise in gdm, 4.6.3. pharmacological treatment, 5. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

• Buchanan, T.A.; Xiang, A.H.; Page, K.A. Gestational Diabetes Mellitus: Risks and Management during and after Pregnancy. Nat. Rev. Endocrinol. 2012 , 8 , 639–649. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crowther, C.A.; Hiller, J.E.; Moss, J.R.; McPhee, A.J.; Jeffries, W.S.; Robinson, J.S.; Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N. Engl. J. Med. 2005 , 352 , 2477–2486. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, H.; Li, N.; Chivese, T.; Werfalli, M.; Sun, H.; Yuen, L.; Hoegfeldt, C.A.; Elise Powe, C.; Immanuel, J.; Karuranga, S.; et al. IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria. Diabetes Res. Clin. Pract. 2022 , 183 , 109050. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kondracki, A.J.; Valente, M.J.; Ibrahimou, B.; Bursac, Z. Risk of large for gestational age births at early, full and late term in relation to pre-pregnancy body mass index: Mediation by gestational diabetes status. Paediatr. Perinat. Epidemiol. 2022 , 36 , 566–576. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, K.W.; Ching, S.M.; Ramachandran, V.; Yee, A.; Hoo, F.K.; Chia, Y.C.; Sulaiman, W.A.W.; Suppiah, S.; Mohamed, M.H.; Veettil, S.K. Prevalence and risk factors of gestational diabetes mellitus in Asia: A systematic review and meta-analysis. BMC Pregnancy Childbirth 2018 , 18 , 494. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McIntyre, H.D.; Catalano, P.; Zhang, C.; Desoye, G.; Mathiesen, E.R.; Damm, P. Gestational diabetes mellitus. Nat. Rev. Dis. Primers 2019 , 5 , 47. [ Google Scholar ] [ CrossRef ]
• Lenoir-Wijnkoop, I.; Van Der Beek, E.M.; Garssen, J.; Nuijten, M.J.C.; Uauy, R.D. Health economic modeling to assess short-term costs of maternal overweight, gestational diabetes, and related macrosomia—A pilot evaluation. Front. Pharmacol. 2015 , 6 , 103. [ Google Scholar ] [ CrossRef ]
• Xu, T.; Dainelli, L.; Yu, K.; Ma, L.; Zolezzi, I.S.; Detzel, P.; Fang, H. The short-term health and economic burden of gestational diabetes mellitus in China: A modelling study. BMJ Open 2017 , 7 , e018893. [ Google Scholar ] [ CrossRef ]
• Plows, J.F.; Stanley, J.L.; Baker, P.N.; Reynolds, C.M.; Vickers, M.H. The Pathophysiology of Gestational Diabetes Mellitus. Int. J. Mol. Sci. 2018 , 19 , 3342. [ Google Scholar ] [ CrossRef ]
• Oliveira, M.M.; Andrade, K.F.O.; Lima, G.H.S.; Rocha, T.C. Metformin versus glyburide in treatment and control of gestational diabetes mellitus: A systematic review with meta-analysis. Einstein 2022 , 20 , eRW6155. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Xu, X.; Yan, Y. Estimated global overweight and obesity burden in pregnant women based on panel data model. PLoS ONE 2018 , 13 , e0202183. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bucley, B.S.; Harreiter, J.; Damm, P.; Corcoy, R.; Chico, A.; Simmons, D.; Vellinga, A.; Dunne, F. Gestational diabetes mellitus in Europe: Prevalence, current screening practice and barriers to screening. Diabet. Med. 2012 , 29 , 844–854. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Moses, R.; Griffits, R.; Daviess, W. Gestational diabetes: Do all women need to be tested? Aust. N. Z. J. Obstet. Gynaecol. 1995 , 35 , 387–389. [ Google Scholar ] [ CrossRef ]
• Lao, T.T.; Ho, L.-F.; Chan, B.C.P.; Leung, W.-C. Maternal Age and Prevalence of Gestational Diabetes Mellitus. Diabetes Care 2006 , 29 , 948–949. [ Google Scholar ] [ CrossRef ]
• Miller, C.; Lim, E. The risk of diabetes after giving birth to a macrosomic infant: Data from the NHANES cohort. Matern. Health Neonatol. Perinatol. 2021 , 7 , 12. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schwartz, N.; Nachum, Z.; Green, S.M. The prevalence of gestational diabetes mellitus recurrence—Effect of ethnicity and parity: A metaanalysis. Am. J. Obstet. Gynecol. 2015 , 213 , 310–317. [ Google Scholar ] [ CrossRef ]
• Kim, C.; Berger, K.D.; Chamany, S. Recurrence of Gestational Diabetes Mellitus A systematic review. Diabetes Care 2007 , 30 , 1314–1319. [ Google Scholar ] [ CrossRef ]
• Torloni, M.R.; Betran, A.P.; Horta, B.L.; Nakamura, M.U.; Atallah, N.A.; Maron, A.F.; Valente, O. Prepregnancy BMI and the risk of gestational diabetes: A systemic review of literature with meta-analysis. Obes. Rev. 2009 , 10 , 194–203. [ Google Scholar ] [ CrossRef ]
• Song, X.; Chen, L.; Zhang, S.; Liu, Y.; Wei, J.; Wang, T.; Qin, J. Gestational Diabetes Mellitus and High Triglyceride Levels Mediate the Association between Pre-Pregnancy Overweight/Obesity and Macrosomia: A Prospective Cohort Study in Central China. Nutrients 2022 , 14 , 3347. [ Google Scholar ] [ CrossRef ]
• Mikola, M.; Hillesmaa, V.; Halttunen, M.; Suhonen, L.; Tiitinen, A. Obstetric outcome in women with polycystic ovarian syndrome. Hum. Reprod. 2001 , 16 , 226–229. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Katsarou, A.; Claesson, R.; Shaat, N.; Ignell, V.; Berntorp, V. Seasonal pattern in the diagnosis of gestational diabetes mellitus in southern Sweden. J. Diabetes Res. 2016 , 2016 , 8905474. [ Google Scholar ] [ CrossRef ]
• Wang, P.; Wu, C.S.; Li, C.Y.; Yang, C.P.; Lu, M.C. Seasonality of gestational diabetes mellitus and maternal blood glucose levels: Evidence from Taiwan. Medicine 2020 , 99 , e22684. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chiefari, E.; Pastore, I.; Puccio, L.; Caroleo, P.; Oliverio, R.; Vero, A.; Foti, D.P.; Vero, R.; Brunetti, A. Impact of seasonality on gestational diabetes mellitus. Endocr. Metab. Immune Disord.-Drug Targets 2017 , 17 , 246–252. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bianchi, C.; Pelle, C.D.; Gennaro, G.D.; Aragona, M.; Cela, V.; Delprato, S.; Bertolotto, A. 1392-P: Assisted Reproduction Technology Treatment and Risk of Gestational Diabetes. Diabetes 2020 , 69 (Suppl. 1), 1392-P. [ Google Scholar ] [ CrossRef ]
• Benhalima, K.; Hanssens, M.; Devlieger, R.; Verhaeghe, J.; Mathieu, C. Analysis of pregnancy outcomes using the new IADPSG recommendation compared with the Carpenter and Coustan criteria in an area with a low prevalence of gestational diabetes. Int. J. Endocrinol. 2013 , 2013 , 248121. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• HAPO Study Cooperative Research Group. Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study: Association with maternal body mass index. BJOG 2010 , 117 , 575–584. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Werner, E.F.; Pettfer, C.M.; Zuckerwise, L.; Reel, M.; Funai, E.F.; Henderson, J.; Thung, S.F. Screening for gestational diabetes mellitus: Are the criteria proposed by the International Association of the Diabetes and Pregnancy Study Groups cost-effective? Diabetes Care 2012 , 35 , 529–535. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Williams, C.B.; Iqbal, S.; Zawacki, C.M.; Yu, D.; Brown, M.B.; Herman, W.H. Effect of selective screening for gestational diabetes. Diabetes Care 1999 , 22 , 418–421. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Landon, M.B.; Spong, C.Y.; Thom, E.; Carpenter, M.W.; Ramin, S.M.; Casey, B.; Wapner, R.J.; Varner, M.W.; Rouse, D.J.; Thorp, J.M., Jr.; et al. A multicenter, randomized trial of treatment for mild gestational diabetes. N. Engl. J. Med. 2009 , 361 , 1339–1348. [ Google Scholar ] [ CrossRef ]
• Horvath, K.; Koch, K.; Jeitler, K.; Matyas, E.; Bender, R.; Bastian, H.; Lange, S.; Siebenhofer, A. Effects of treatment in women with gestational diabetes mellitus: Systemic review and meta-analysis. BMJ 2010 , 340 , c1395. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Aubry, E.M.; Raio, L.; Oelhafen, S. Effect of the IADPSG screening strategy for gestational diabetes on perinatal outcomes in Switzerland. Diabetes Res. Clin. Pract. 2021 , 175 , 108830. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hillier, T.A.; Pedula, K.L.; Ogasawara, K.K.; Vesco, K.K.; Oshiro, C.E.S.; Lubarsky, S.L.; Van Marter, J. A Pragmatic, Randomized Clinical Trial of Gestational Diabetes Screening. N. Engl. J. Med. 2021 , 384 , 895–904. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crowther, C.A.; Samuel, D.; McCowan, L.M.; Edlin, R.; Tran, T.; McKinlay, C.J. Lower versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes. N. Engl. J. Med. 2022 , 387 , 587–598. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lorenzo-Almoros, A.; Hang, T.; Peiro, C.; Soriano-Guillen, L.; Egido, J.; Tunon, J.; Lorenzo, O. Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases. Cardiovasc. Diabetol. 2019 , 18 , 140. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Corcoran, M.S.; Achamallah, N.; O’Loughlin, J.; Stafford, P.; Dicker, P.; Malone, D.F.; Breathnach, F. First trimester serum biomarkers to predict gestational diabetes in a high-risk cohort: Striving for clinically useful thresholds. Eur. J. Obstet. Gynecol. Reprod. Biol. 2018 , 222 , 7–12. [ Google Scholar ] [ CrossRef ]
• Kotzaeridi, G.; Blätter, J.; Eppel, D.; Rosicky, I.; Mittlböck, M.; Yerlikaya-Schatten, G.; Schatten, C.; Husslein, P.; Eppel, W.; Huhn, E.A.; et al. Performance of early risk assessment tools to predict the later development of gestational diabetes. Eur. J. Clin. Investig. 2021 , 51 , e13630. [ Google Scholar ] [ CrossRef ]
• Yong, H.Y.; Shariff, Z.M.; Yusof, B.N.M.; Rejali, Z.; Tee, Y.Y.S.; Bindels, J.; Van Der Beek, E.M. Independent and combined effects of age, body mass index and gestational weight gain on the risk of gestational diabetes mellitus. Sci. Rep. 2020 , 10 , 8486. [ Google Scholar ] [ CrossRef ]
• Homko, C.; Sivan, E.; Chen, X.; Reece, E.A.; Boden, G. Insulin secretion during and after pregnancy in patients with gestational diabetes mellitus. J. Clin. Endocrinol. Metab. 2001 , 86 , 568–573. [ Google Scholar ] [ CrossRef ]
• Baeyens, L.; Hindi, S.; Sorenson, R.L.; German, M.S. β-cell adaptation in pregnancy. Diabetes Obes. Metab. 2016 , 18 (Suppl. 1), 63–70. [ Google Scholar ] [ CrossRef ]
• Baz, B.; Riverline, J.P.; Gautier, J.F. Endocrinology of pregnancy: Gestational Diabetes Mellitus: Definition, aetiological and clinical aspects. Eur. J. Endocrinol. 2016 , 174 , R43–R51. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Barberoglu, Z. Pathophysiology of gestational diabetes mellitus. EMJ Diabetes 2019 , 7 , 97–106. [ Google Scholar ]
• Buchanan, T.A.; Kijos, S.L.; Xiang, A.; Watanabe, R. What is Gestational Diabetes? Diabetes Care 2007 , 30 (Suppl. 2), 105–111. [ Google Scholar ] [ CrossRef ]
• Barbour, L.A.; McCurdy CEHernandez, T.L.; Kirwan, J.P.; Catalano, P.M.; Friedman, J.E. Cellular mechanizm of insulin resistance in normal pregnancy and Gestational Diabetes. Diabetes Care 2007 , 30 (Suppl. 2), 112–119. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vrachins, N.; Belitsos, P.; Sifakis, S.; Dafopoulos, K.; Siristatidis, C.; Pappa, K.I.; Iliodromiti, Z. Role of adipokines and other inflammatory mediators in gestational diabetes mellitus and previous gestational diabetes mellitus. Int. J. Endocrinol. 2012 , 2012 , 549748. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kirwan, J.P.; Hauguel-De Mouzon, S.; Lepercq, J.; Challier, J.-C.; Huston-Presley, L.; Friedman, J.E.; Kalhan, S.C.; Catalano, P.M. TNF-α is a predictor of insulin resistance in human pregnancy. Diabetes 2002 , 51 , 2207–2213. [ Google Scholar ] [ CrossRef ]
• Qiu, C.; Williams, M.A.; Vadachkoria, S.; Frederick, I.O.; Luthy, D.A. Increased maternal plasma leptin in early pregnancy and risk of gestational diabetes mellitus. Obstet. Gynecol. 2004 , 103 , 519–525. [ Google Scholar ] [ CrossRef ]
• Honnorat, D.; Disse, E.; Millot, L.; Mathiotte, E.; Claret, M.; Charrié, A.; Drai, J.; Garnier, L.; Maurice, C.; Durand, E.; et al. Are third-trimester adipokines associated with higher metabolic risk among women with gestational diabetes? Diabetes Metab. 2015 , 41 , 393–400. [ Google Scholar ] [ CrossRef ]
• Maple-Brown, L.; Ye, C.; Hanley, A.J.; Connelly, P.W.; Sermer, M.; Zinman, B.; Retnakaran, R. Maternal pregravid weight is the primary determinant of serum leptin and its metabolic associations in pregnancy, irrespective of gestational glucose tolerance status. J. Clin. Endocrinol. Metab. 2012 , 97 , 4148–4155. [ Google Scholar ] [ CrossRef ]
• Miehle, K.; Stepan, H.; Fasshauer, M. Leptin, adiponectin and other adipokines in gestational diabetes and pre-eclampsia. Clin. Endocrinol. 2012 , 76 , 2–11. [ Google Scholar ] [ CrossRef ]
• Valencia-Ortega, J.; González-Reynoso, R.; Ramos-Martínez, E.G.; Ferreira-Hermosillo, A.; Peña-Cano, M.I.; Morales-Ávila, E.; Saucedo, R. New Insights into Adipokines in Gestational Diabetes Mellitus. Int. J. Mol. Sci. 2022 , 23 , 6279. [ Google Scholar ] [ CrossRef ]
• Kumar, A.d.L.A.; Corcoy, R. Autoimmunity in Gestational Diabetes A Decade after the HAPO Study. Front. Diabetes 2020 , 28 , 234–242. [ Google Scholar ] [ CrossRef ]
• Gjesing, A.; Rui, G.; Launeborg, J.; Have, C.H.; Hollensted, M.; Andersson, E.; Grarup, N.; Sun, J.; Quan, S.; Brandslund, I. High Prevalence of Diabetes-Predisposing Variants in MODY Genes Among Danish Women With Gestational Diabetes Mellitus. J. Endocr. Soc. 2017 , 1 , 681–690. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mao, H.; Li, Q.; Gao, S. Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus. PLoS ONE 2012 , 7 , e45882-6. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lowe, W.L.; Scholtens, D.M.; Sandler, V.; Hayes, M.G. Genetics of gestational diabetes mellitus and maternal metabolism. Curr. Diabetes Rep. 2016 , 16 , 15–24. [ Google Scholar ] [ CrossRef ]
• Barabash, A.; Valerio, J.D.; de la Torre, N.G.; Jimenez, I.; del Valle, L.; Melero, V.; Assaf-Balut, C.; Fuentes, M.; Bordiu, E.; Durán, A.; et al. TCF7L2 rs7903146 polymorphism modulates the association between adherence to a Mediterranean diet and the risk of gestational diabetes mellitus. Metab. Open 2020 , 8 , 100069. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ott, R.; Melchior, K.; Stupin, J.H.; Ziska, T.; Schellong, K.; Henrich, W.; Rancourt, R.C.; Plagemann, A. Reduced insulin receptor expression and altered DNA methylation in fat tissue and blood of women with GDM and offspring. J. Clin. Endocrinol. Metab. 2019 , 104 , 137–149. [ Google Scholar ] [ CrossRef ]
• Reichetzeder, C.; Dwi Putra, S.E.; Pfab, T.; Slovinski, T.; Neuber, C.; Kleuser, B.; Hocher, B. Increased global placental DNA methylation levels are associated with gestational diabetes. Clin. Epigenet. 2016 , 8 , 82. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Chen, Y.; Qu, H.; Wang, Y. Methylation of HIF3A promoter CpG islands contributes to insulin resistance in gestational diabetes mellitus. Mol. Genet. Genom. Med. 2019 , 7 , e00583. [ Google Scholar ] [ CrossRef ]
• Assi, E.; D’Addio, F.; Mandò, C.; Maestroni, A.; Loretelli, C.; Ben Nasr, M.; Usuelli, V.; Abdelsalam, A.; Seelam, A.J.; Pastore, I.; et al. Placental proteome abnormalities in women with gestational diabetes and large-for-gestational-age newborns. BMJ Open Diabetes Res. Care 2020 , 8 , e001586. [ Google Scholar ] [ CrossRef ]
• Khosrowbeygi, A.; Rezvanfar, M.R.; Ahmadvand, H. Tumor necrosis factor-α, adiponectin and their ratio in gestational diabetes mellitus. Casp. J. Intern. Med. 2018 , 9 , 71–79. [ Google Scholar ] [ CrossRef ]
• Allotey, J.; Stallings, E.; Bonet, M. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: Living systematic review and meta-analysis. BMJ 2020 , 370 , 1–18. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nouhjah, S.; Jahanfar, S.; Shahbazian, H. Temporary changes in clinical guidelines of gestational diabetes screening and management during COVID-19 outbreak: A narrative review. Diabetes Metab. Syndr. 2020 , 14 , 939–942. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van Gemert, E.T.; Moses, G.R.; Pape, V.A.; Morris, J.G. Gestational diabetes mellitus testing in the Covid19 pandemic: The problems with simplifying the diagnostic process. Aust. N. Z. J. Obstet. Gynaecol. 2020 , 60 , 671–674. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Simmons, D.; Rudland, L.V.; Wong, V.; Flack, J.; Mackie, A.; Ross, P.G.; Coat, S.; Dalal, R.; Hague, M.B.; Cheung, W.N. Options for screening for gestational diabetes mellitus during the SARS-CoV-2 pandemic. Aust. N. Z. J. Obstet. Gynaecol. 2020 , 60 , 660–666. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McIntyre, H.D.; Gibbons, S.K.; Ma, C.W.R.; Tam, H.W.; Sacks, A.D.; Lowe, J.; Madsen, R.L.; Catalano, M.P. Testing for gestational diabetes during the COVID-19 pandemic. An evaluation of proposed protocols for the United Kingdom, Canada and Australia. Diabetes Res. Clin. Pract. 2020 , 167 , 108353. [ Google Scholar ] [ CrossRef ]
• McIntyre, H.D.; Moses, R.G. The diagnosis and management of gestational diabetes mellitus in the context of the COVID-19 pandemic. Diabetes Care 2020 , 43 , 1433–1434. [ Google Scholar ] [ CrossRef ]
• Zanardo, V.; Tortora, D.; Sandri, A.; Severino, L.; Mesirca, P.; Straface, G. COVID-19 pandemic: Impact on gestational diabetes mellitus prevalence. Diabetes Res. Clin. Pract. 2022 , 183 , 109149. [ Google Scholar ] [ CrossRef ]
• Hillyard, M.; Sinclair, M.; Murphy, M.; Casson, K.; Mulligan, C. The impact of COVID-19 on the physical activity and sedentary behaviour levels of pregnant women with gestational diabetes. PLoS ONE 2021 , 16 , e0254364. [ Google Scholar ] [ CrossRef ]
• Eberle, C.; Stichling, S. Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus: A systematic review. Diabetol. Metab. Syndr. 2021 , 13 , 95. [ Google Scholar ] [ CrossRef ]
• Ghesquière, L.; Garabedian, C.; Drumez, E.; Lemaître, M.; Cazaubiel, M.; Bengler, C.; Vambergue, A. Effects of COVID-19 pandemic lockdown on gestational diabetes mellitus: A retrospective study. Diabetes Metab. 2021 , 47 , 101201. [ Google Scholar ] [ CrossRef ]
• Martis, R.; Brown, J.; Alsweiler, J.; Crawford, T.J.; Crowther, C.T. Different intensities of glycaemic control for women with gestational diabetes mellitus. Cochrane Database Syst. Rev. 2016 , 4 , CD011624. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mitanchez, D.; Ciangura, C.; Jacqueminet, S. How can maternal lifestyle interventions modify the effects of gestational diabetes in the neonate and the offspring? A systematic review of meta-analyses. Nutrients 2020 , 12 , 353. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• American Diabetes Association. 14. Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes—2020. Diabetes Care 2019 , 43 , S183–S192. [ Google Scholar ]
• Chao, H.; Chen, G.; Wen, X.; Liu, J.; Zhang, J. Dietary control plus nutrition guidance for blood glucose and pregnancy outcomes in women with gestational diabetes. Int. J. Clin. Exp. Med. 2019 , 12 , 2773–2778. [ Google Scholar ]
• Morris, M.A.; Hutchinson, J.; Gianfrancesco, C.; Alwan, N.A.; Carter, M.C.; Scott, E.M.; Cade, J.E. Relationship of the frequency, distribution, and content of meals/snacks to glycaemic control in gestational diabetes: The myfood24 GDM pilot study. Nutrients 2020 , 12 , 3. [ Google Scholar ] [ CrossRef ]
• Hay, W.W. Placental-Fetal Glucose Exchange and Fetal Glucose Metabolism. Trans. Am. Clin. Clim. Assoc. 2006 , 117 , 321–340. [ Google Scholar ]
• Nordic Nutrition of Ministers. Nordic Nutrition Recommendations 2012 , 5th ed.; Norden: Copenhagen, Denmark, 2014; pp. 1–629. [ Google Scholar ]
• Yaktine, A.L.; Rasmussen, K.M.; Youth, F.; National Research Council; Institute of Medicine; Board on Children; Committee to Reexamine IOM Pregnancy Weight Guidelines. Weight Gain During Pregnancy: Reexamining the Guidelines (2009) ; Rasmussen, K.M., Yaktine, A.L., Eds.; The National Academies Press: Washington, DC, USA, 2009. [ Google Scholar ]
• Jamilian, M.; Asemi, Z. The Effect of Soy Intake on Metabolic Profiles of Women with Gestational Diabetes Mellitus. J. Clin. Endocrinol. Metab. 2015 , 100 , 4654–4661. [ Google Scholar ] [ CrossRef ]
• Rasmussen, L.; Poulsen, C.W.; Kampmann, U.; Smedegaard, S.B.; Ovesen, P.G.; Fuglsang, J. Diet and Healthy Lifestyle in the Management of Gestational Diabetes Mellitus. Nutrients 2020 , 12 , 3050. [ Google Scholar ] [ CrossRef ]
• Hernandez, T.L.; Van Pelt, R.E.; Anderson, M.A.; Daniels, L.J.; West, N.A.; Donahoo, W.T.; Friedman, J.E.; Barbour, L.A. A Higher-Complex Carbohydrate Diet in Gestational Diabetes Mellitus Achieves Glucose Targets and Lowers Postprandial Lipids: A Randomized Crossover Study. Diabetes Care 2014 , 37 , 1254–1262. [ Google Scholar ] [ CrossRef ]
• Danielewicz, H.; Myszczyszyn, G.; Debinska, A.; Myszkal, A.; Boznanaski, A.; Hirnle, L. Diet in pregnancy—More than food. Eur. J. Pediatr. 2017 , 176 , 1573–1579. [ Google Scholar ] [ CrossRef ]
• Elshani, B.; Kotori, V.; Daci, A. Role of omega-3 polyunsaturated fatty acids in gestational diabetes, maternal and fetal insights: Current use and future directions. J. Matern.-Fetal Neonatal Med. 2019 , 34 , 124–136. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kiel, D.; Dodson, E.; Artal, R.; Boehmer, T.; Leet, T. Gestational Weight Gain and Pregnancy Outcomes in Obese Women How Much Is Enough? Obstet. Gynecol. 2007 , 110 , 752–758. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sun, Y.; Shen, Z.; Zhan, Y. Effects of pre-pregnanacy body mass index and gestational weight gain on maternal and infant complications. BMC Pregnancy Childbirth 2020 , 20 , 390. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bouter, K.E.; Van Raalte, D.H.; Groen, A.K.; Nieuwdorp, M. Role of the Gut Microbiome in the Pathogenesis of Obesity and Obesity-Related Metabolic Dysfunction. Gastroenterology 2017 , 152 , 1671–1678. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crusell, M.K.W.; Hansen, T.H.; Nielsen, T.S.; Allin, K.H.; Rühlemann, M.C.; Damm, P.; Vestergaard, H.; Rørbye, C.; Jørgensen, N.R.; Christiansen, O.B.; et al. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome 2018 , 6 , 89. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pellonperä, O.; Mokkala, K.; Houttu, N.; Vahlberg, T.; Koivuniemi, E.; Tertti, K.; Rönnemaa, T.; Laitinen, K. Ecacy of Fish Oil and/or Probiotic Intervention on the Incidence of Gestational Diabetes Mellitus in an At-Risk Group of Overweight and ObeseWomen: A Randomized, Placebo-Controlled, Double-Blind Clinical Trial. Diabetes Care 2019 , 42 , 1009–1017. [ Google Scholar ] [ CrossRef ]
• Callaway, L.K.; McIntyre, H.D.; Barrett, H.L.; Foxcroft, K.; Tremellen, A.; Lingwood, B.E.; Tobin, J.M.; Wilkinson, S.A.; Kothari, A.; Morrison, M.; et al. Probiotics for the Prevention of Gestational Diabetes Mellitus in Overweight and ObeseWomen: Findings From the SPRING Double-blind Randomized Controlled Trial. Diabetes Care 2019 , 42 , dc182248. [ Google Scholar ] [ CrossRef ]
• Pan, J.; Pan, Q.; Chen, Y.; Zhang, H.; Zheng, X. Ecacy of probiotic supplement for gestational diabetes mellitus: A systematic review and meta-analysis. J. Matern.-Fetal Neonatal Med. 2017 , 32 , 317–323. [ Google Scholar ] [ CrossRef ]
• Kijmanawat, A.; Panburana, P.; Reutrakul, S.; Tangshewinsirikul, C. Efects of probiotic supplements on insulinresistance in gestational diabetes mellitus: A double-blind randomized controlled trial. J. Diabetes Investig. 2018 , 10 , 163–170. [ Google Scholar ] [ CrossRef ]
• Asemi, Z.; Samimi, M.; Tabassi, Z.; Esmaillzadeh, A. The effect of DASH diet on pregnancy outcomes in gestational diabetes: A randomized controlled clinical trial. Eur. J. Clin. Nutr. 2014 , 68 , 490–495. [ Google Scholar ] [ CrossRef ]
• Sarathi, V.; Kolly, A.; Chaithanya, H.B.; Dwarakanath, C.S. Effect of soya based protein rich diet on glycaemic parameters and thyroid function tests in women with gestational diabetes mellitus. Rom. J. Diabetes Nutr. Metab. Dis. 2016 , 23 , 201–208. [ Google Scholar ] [ CrossRef ]
• Guardo, F.D.; Curro, J.M.; Valenti, G.; Rossetti, P.; Di Gregorio, L.M.; Conway, F.; Chiofalo, B.; Garzon, S.; Bruni, S.; Rizzo, G. Non-pharmacological management of gestational diabetes: The role of myo-inositol. J. Complement. Integr. Med. 2019 , 17 . [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Crawford, T.J.; Alsweiler, J.; Crowther, C.A. Dietary supplementation with myo-inositol in women during pregnancy for treating gestational diabetes. Cochrane Database Syst. Rev. 2016 , 9 , CD012048. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Ceysens, G.; Boulvain, M. Exercise for pregnant women with gestational diabetes for improving maternal and fetal outcomes. Cochrane Database Syst. Rev. 2017 , 6 , CD012202. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• American College of Obstetricians and Gynecologists. ACOG Committee Opinion Number 650, December 2015. Physical Activity and Exercise During Pregnancy and the Postpartum Period. Available online: http://www.acog.org/Resources-And-Publications/Committee-Opinions/Committee-on-Obstetric-Practice/Physical-Activity-and-Exercise-During-Pregnancy-and-the-Postpartum-Period (accessed on 1 December 2015).
• Aune, D.; Sen, A.; Henriksen, T.; Saugstad, O.D.; Tonstad, S. Physical activity and the risk of gestational diabetes mellitus: A systemic review and dose-response meta-analysis of epidemiological studies. Eur. J. Epidemiol. 2016 , 31 , 967–997. [ Google Scholar ] [ CrossRef ]
• Nasiri-Amiri, F.; Sepidarkish, M.; Shirvani, M.A.; Habibipour, P.; Tabari, N.S. The effect of exercise on the prevention of gestational diabetes in obese and overweight pregnant women: A systemic review and meta-analysis. Diabetol. Metab. Syndr. 2019 , 11 , 72. [ Google Scholar ] [ CrossRef ]
• Ming, W.K.; Ding, W.; Zhang, C.J.; Zhong, L.; Long, Y.; Li, Z.; Sun, C.; Wu, Y.; Chen, H.; Chen, H.; et al. The effect of exercise during pregnancy on gestational diabetes mellitus in normal-weight women: A systemic review and meta-analysis. BMC Pregnancy Childbirth 2018 , 18 , 440. [ Google Scholar ] [ CrossRef ]
• Harrison, A.L.; Shields, N.; Taylor, N.; Frawley, H.C. Exercise improves glycaemic control in women diagnosed with gestational diabetes mellitus: A systematic review. J. Physiother. 2016 , 62 , 188–196. [ Google Scholar ] [ CrossRef ]
• Nguyen, L.; Chan, S.Y.; Teo, A.K. Metformin from mother to unborn child-are there unwarranted effects? EbioMedicine 2018 , 35 , 394–404. [ Google Scholar ] [ CrossRef ]
• Blum, A.K. Insulin Use in Pregnancy: An Update. Diabetes Spectr. 2016 , 29 , 92–97. [ Google Scholar ] [ CrossRef ]
• Hod, M.; Mathiesen, E.R.; Jovanovič, L.; McCance, D.R.; Ivanisevic, M.; Duran-Garcia, S.; Brøndsted, L.; Nazeri, A.; Damm, P. A randomized trial comparing perinatal outcomes using insulin detemir or neutral protamine Hagedorn in type 1 diabetes. J. Matern.-Fetal Neonatal Med. 2014 , 27 , 7–13. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mathiesen, E.R.; Andersen, H.; Kring, S.I.; Damm, P. Design and rationale of a large, international, prospective cohort study to evaluate the occurrence of malformations and perinatal/neonatal death using insulin detemir in pregnant women with diabetes in comparison with other long-acting insulins. BMC Pregnancy Childbirth 2017 , 17 , 38. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hod, M.; Damm, P.; Kaaja, R.; Visser, G.H.; Dunne, F.; Demidova, I.; Pade Hansen, A.-S.; Mersebach, H. Fetal and perinatal outcomes in type 1 diabetes pregnancy: A randomized study comparing insulin aspart with human insulin in 322 subjects. Am. J. Obstet. Gynecol. 2007 , 198 , 186-e1. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pantalone, K.M.; Failman, C.; Olansky, L. Insulin Glargine Use During Pregnancy. Endocr. Pract. 2011 , 17 , 448–455. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Grzeskowiak, L.; Williamson, K.; Downie, M.R.; Crowther, C.A. Insulin for the treatment of women with gestational diabetes. Cochrane Database Syst. Rev. 2017 , 11 , CD012037. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Norgaard, K.; Sukumar, N.; Raffnson, S.B.; Saravanan, P. Efficacy and safety of rapid-acting insulin analogs in special populations with type 1 diabetes or gestational diabetes: Systemic review and meta-analysis. Diabetes Ther. 2018 , 9 , 891–917. [ Google Scholar ] [ CrossRef ]
• Mukerji, G.; Feig, D.S. Advances in Oral Anti-Diabetes Drugs in Pregnancy. Pract. Man. Diabetes Pregnancy 2017 , 15 , 189–201. [ Google Scholar ] [ CrossRef ]
• Lee, H.Y.; Wei, D.; Loeken, M.R. Lack of metformin effect on mouse embryo AMPK activity: Implications for metformin treatment during pregnancy. Diabetes/Metab. Res. Rev. 2014 , 30 , 23–30. [ Google Scholar ] [ CrossRef ]
• Rowan, J.A.; Hague, W.M.; Gao, W.; Battin, M.R.; Moore, M.P. Metformin versus insulin for the treatment of gestational diabetes. N. Engl. J. Med. 2008 , 358 , 2003–2015. [ Google Scholar ] [ CrossRef ]
• Rowan, J.; Rush, C.E.; Plank, L.D.; Lu, J.; Obolonkin, V.; Coat, S.; Hague, W.M. Metformin in gestational diabetes: The offspring follow-up (MiG TOFU): Body composition and metabolic outcomes at 7–9 years of age. BMJ Open Diabetes Res. Care 2018 , 6 , e000456. [ Google Scholar ] [ CrossRef ]
• Zeng, Y.; Li, M.; Chen, Y.; Jianng, L.; Wang, S.; Mo, X.; Li, B. The use of glyburide in the management of gestational diabetes mellitus: A meta-analysis. Adv. Med. Sci. 2014 , 59 , 95–101. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, D.-Q.; Xu, G.-X.; Teng, X.-Y.; Xu, J.-W.; Tang, L.-F.; Feng, C.; Rao, J.-P.; Jin, M.; Wang, L.-Q. Glycemic control and neonatal outcomes in women with gestational diabetes mellitus treated using glyburide, metformin, or insulin: A pairwise and network meta-analysis. BMC Endocr. Disord. 2021 , 21 , 199. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schneeberger, C.; Kazemier, B.M.; Geerlings, S.E. Asymptomatic bacteriuria and urinary tract infections in special patient groups: Women with diabetes mellitus and pregnant women. Curr. Opin. Infect. Dis. 2014 , 27 , 108–114. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• White, W.B.; Cannon, C.P.; Heller, S.R.; Nissen, S.E.; Bergenstal, R.M.; Bakris, G.L.; Perez, A.T.; Fleck, P.R.; Mehta, C.R.; Kupfer, S.; et al. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N. Engl. J. Med. 2013 , 369 , 1327–1335. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Marso, S.P.; Daniels, G.H.; Brown-Frandsen, K.; Kristensen, P.; Mann, J.F.E.; Nauck, M.A.; Nissen, S.E.; Pocock, S.; Poulter, N.R.; Ravn, L.S.; et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N. Engl. J. Med. 2016 , 375 , 311–322. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, C.; Huang, Y.; Dong, G.; Zeng, Y.; Zhou, Z. The effect of dipeptidyl peptidase-4 inhibitor and glucagon-like peptide-1 receptor agonist in gestational diabetes mellitus: A systematic review. Gynecol. Endocrinol. 2020 , 36 , 375–380. [ Google Scholar ] [ CrossRef ]
• Simmons, D.; Nema, J.; Parton, C.; Vizza, L.; Robertson, A.; Rajagopal, R.; Ussher, J.; Perz, J. The treatment of booking gestational diabetes mellitus (TOBOGM) pilot randomised controlled trial. BMC Pregnancy Childbirth 2018 , 18 , 151. [ Google Scholar ] [ CrossRef ]

Share and Cite

Modzelewski, R.; Stefanowicz-Rutkowska, M.M.; Matuszewski, W.; Bandurska-Stankiewicz, E.M. Gestational Diabetes Mellitus—Recent Literature Review. J. Clin. Med. 2022 , 11 , 5736. https://doi.org/10.3390/jcm11195736

Modzelewski R, Stefanowicz-Rutkowska MM, Matuszewski W, Bandurska-Stankiewicz EM. Gestational Diabetes Mellitus—Recent Literature Review. Journal of Clinical Medicine . 2022; 11(19):5736. https://doi.org/10.3390/jcm11195736

Modzelewski, Robert, Magdalena Maria Stefanowicz-Rutkowska, Wojciech Matuszewski, and Elżbieta Maria Bandurska-Stankiewicz. 2022. "Gestational Diabetes Mellitus—Recent Literature Review" Journal of Clinical Medicine 11, no. 19: 5736. https://doi.org/10.3390/jcm11195736

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Metabolic Diseases
• Glucose Metabolism Disorders
• Gestational Diabetes

Gestational Diabetes Mellitus—Recent Literature Review

• 11(19):5736
• This person is not on ResearchGate, or hasn't claimed this research yet.

• Wojewódzki Szpital Specjalistyczny w Olsztynie, Poland

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Ramona E Dragomir
• Daniela E Gheoca Mutu
• Romina M Sima
• Ruxandra V Stănculescu

• Lidia Mínguez-Alarcón
• Rachel S. Kelly

• Abdul Wahab Alshaban
• Hani Faysal
• Mitchell Grecu
• Katherine Connelly
• Katarzyna Molenda
• Karol Bochyński

• Michał Dacka
• Vidyasri Bailore
• Kalpana Basany
• Maheshwari Banda
• Ruifang Wang
• Junmiao Xiang
• Prachi Joshi
• Richa Gupta
• Kamna Singh
• Tanu Kardam

• Senmao Zhang

• INT J MOL SCI

• Marina Martins de Oliveira
• Kayan Felipe de Oliveira Andrade
• Giovanni Henrique Silva Lima
• Thiago Casali Rocha

• Zoran Bursac
• BMC Endocr Disord
• Dan-qing Yu
• Guan-Xin Xu
• Xin-Yuan Teng
• Li-Quan Wang
• Caroline A Crowther
• Deborah Samuel

• DIABETES RES CLIN PR

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

• Previous Article
• Next Article

Introduction

Research design and methods, conclusions, article information, gestational diabetes mellitus and diet: a systematic review and meta-analysis of randomized controlled trials examining the impact of modified dietary interventions on maternal glucose control and neonatal birth weight.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Get Permissions

Jennifer M. Yamamoto , Joanne E. Kellett , Montserrat Balsells , Apolonia García-Patterson , Eran Hadar , Ivan Solà , Ignasi Gich , Eline M. van der Beek , Eurídice Castañeda-Gutiérrez , Seppo Heinonen , Moshe Hod , Kirsi Laitinen , Sjurdur F. Olsen , Lucilla Poston , Ricardo Rueda , Petra Rust , Lilou van Lieshout , Bettina Schelkle , Helen R. Murphy , Rosa Corcoy; Gestational Diabetes Mellitus and Diet: A Systematic Review and Meta-analysis of Randomized Controlled Trials Examining the Impact of Modified Dietary Interventions on Maternal Glucose Control and Neonatal Birth Weight. Diabetes Care 1 July 2018; 41 (7): 1346–1361. https://doi.org/10.2337/dc18-0102

Download citation file:

• Ris (Zotero)
• Reference Manager

Medical nutrition therapy is a mainstay of gestational diabetes mellitus (GDM) treatment. However, data are limited regarding the optimal diet for achieving euglycemia and improved perinatal outcomes. This study aims to investigate whether modified dietary interventions are associated with improved glycemia and/or improved birth weight outcomes in women with GDM when compared with control dietary interventions.

Data from published randomized controlled trials that reported on dietary components, maternal glycemia, and birth weight were gathered from 12 databases. Data were extracted in duplicate using prespecified forms.

From 2,269 records screened, 18 randomized controlled trials involving 1,151 women were included. Pooled analysis demonstrated that for modified dietary interventions when compared with control subjects, there was a larger decrease in fasting and postprandial glucose (−4.07 mg/dL [95% CI −7.58, −0.57]; P = 0.02 and −7.78 mg/dL [95% CI −12.27, −3.29]; P = 0.0007, respectively) and a lower need for medication treatment (relative risk 0.65 [95% CI 0.47, 0.88]; P = 0.006). For neonatal outcomes, analysis of 16 randomized controlled trials including 841 participants showed that modified dietary interventions were associated with lower infant birth weight (−170.62 g [95% CI −333.64, −7.60]; P = 0.04) and less macrosomia (relative risk 0.49 [95% CI 0.27, 0.88]; P = 0.02). The quality of evidence for these outcomes was low to very low. Baseline differences between groups in postprandial glucose may have influenced glucose-related outcomes. As well, relatively small numbers of study participants limit between-diet comparison.

Modified dietary interventions favorably influenced outcomes related to maternal glycemia and birth weight. This indicates that there is room for improvement in usual dietary advice for women with GDM.

Gestational diabetes mellitus (GDM) is one of the most common medical complications in pregnancy and affects an estimated 14% of pregnancies, or one in every seven births globally ( 1 ). Women with GDM and their offspring are at increased risk of both short- and longer-term complications, including, for mothers, later development of type 2 diabetes, and for offspring, increased lifelong risks of developing obesity, type 2 diabetes, and metabolic syndrome ( 2 – 6 ). The adverse intrauterine environment causes epigenetic changes in the fetus that may contribute to metabolic disorders, the so-called vicious cycle of diabetes ( 7 ).

The mainstay of GDM treatment is dietary and lifestyle advice, which includes medical nutrition therapy, weight management, and physical activity ( 8 ). Women monitor their fasting and postmeal glucose levels and adjust their individual diet and lifestyle to meet their glycemic targets. This pragmatic approach achieves the glycemic targets in approximately two-thirds of women with GDM ( 8 ). However, despite the importance of medical nutrition therapy and its widespread recommendation in clinical practice, there are limited data regarding the optimal diet for achieving maternal euglycemia ( 8 – 11 ). It is also unknown whether the dietary interventions for achieving maternal glycemia are also effective for reducing excessive fetal growth and adiposity ( 12 ).

Different dietary strategies have been reported including low glycemic index (GI), energy restriction, increase or decrease in carbohydrates, and modifications of fat or protein quality or quantity ( 12 – 14 ). Three recent systematic reviews have been performed examining specific diets and pregnancy outcomes ( 15 – 17 ). Viana et al. ( 16 ) and Wei et al. ( 15 ) concluded that low-GI diets were associated with a decreased risk of infant macrosomia. However, the most recent systematic review from Cochrane, including 19 trials randomizing 1,398 women, found no clear difference in large for gestational age or other primary neonatal outcomes with the low-GI diet ( 17 ). The primary maternal outcomes were hypertension (gestational and/or preeclampsia), delivery by cesarean section, and type 2 diabetes, outcomes for which most trials lacked statistical power, even when dietary subgroups were combined. Remarkably, no systematic reviews examined the impact of modified dietary interventions on the detailed maternal glycemic parameters, including change in glucose-related variables, the outcomes that are most directly influenced by diet.

To address this knowledge gap, we performed a systematic review and meta-analysis of randomized controlled trials to investigate whether modified dietary interventions (defined as a dietary intervention different from the usual one used in the control group) in women with GDM offer improved glycemic control and/or improved neonatal outcomes when compared with standard diets.

In accordance with a published protocol (PROSPERO CRD42016042391), we performed a systematic review and meta-analysis. Reporting is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 18 ). An international panel of experts was formed by the International Life Sciences Institute Europe. This panel determined the review protocol and carried out all aspects of the review.

Data Sources and Search Strategy

The following databases were searched for all available dates using the search terms detailed in Supplementary Table 1 : PubMed, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science Core Collection, Applied Social Sciences Index and Abstracts, ProQuest, ProQuest Dissertations & Theses—A&I and UK & Ireland, National Institute for Health and Care Excellence evidence search, Scopus, UK Clinical Trials Gateway, ISRCTN, and ClinicalTrials.gov . The initial search was performed in July 2016. An updated search of MEDLINE, Embase, CENTRAL, and CINAHL was performed on 3 October 2017 using the same search terms.

A hand-search of relevant reviews and all included articles was conducted to identify studies for potential inclusion. As well, experts on the panel were consulted for the inclusion of additional articles. Reference management was carried out using EndNote.

Study Selection

All titles and abstracts were assessed independently and in duplicate to identify articles requiring full-text review. Published studies fulfilling the following criteria were included: randomized controlled trials, evaluated modified dietary interventions on women with GDM, glucose intolerance or hyperglycemia during pregnancy, reported-on primary maternal and neonatal outcomes, included women aged 18–45 years, had a duration of 2 weeks or more, and were published in English, French, Spanish, Portuguese, Italian, Dutch, German, or Chinese. We excluded studies that included participants with type 1 or type 2 diabetes if data for participants with GDM were not presented independently, if dietary characteristics were not available, if the study was in animals, or if the study did not report outcomes of interest. We did not include studies of nutritional supplements such as vitamin D or probiotics as recent reviews have addressed these topics ( 19 , 20 ).

All citations identified after title and abstract assessment were full-text reviewed in duplicate. Reasons for exclusion at the full-text review stage were recorded. Any disagreements between reviewers were resolved by consensus and with consultation with the expert group when required.

Data Extraction

Data from included studies were extracted in duplicate using prespecified data extraction forms. Extracted data elements included study and participant demographics, study design, diagnostic criteria for GDM, glucose intolerance or hyperglycemia, funding source, description of modified dietary intervention and comparator, and maternal and neonatal outcomes. For studies with missing data, inconsistencies, or other queries, authors were contacted. Record management was carried out using Microsoft Excel and RevMan.

For articles providing information on maternal weight, fasting glucose, postprandial glucose, HbA 1c , or HOMA insulin resistance index (HOMA-IR) at baseline and postintervention but not their change, change was calculated as the difference between postintervention and baseline. Standard deviations were imputed using the correlation coefficient observed in articles reporting full information on the variable at baseline and postintervention and its change or a correlation coefficient of 0.5 when this information was not available ( 21 ). As studies differed in postprandial glucose at baseline, glycemic control at study entry was not considered to be equivalent in both arms, and thus continuous glucose-related variables at follow-up are reported as change from baseline.

Data Synthesis

The primary outcomes were maternal glycemic outcomes (mean glucose, fasting glucose, postprandial glucose [after breakfast, lunch, and dinner and combined], hemoglobin A 1c [HbA 1c ], assessment of insulin sensitivity by HOMA-IR, and change in these parameters from baseline to assessment; medication treatment [defined as oral diabetes medications or insulin]) and neonatal birth weight outcomes (birth weight, macrosomia, and large for gestational age).

Data were pooled into relative risks (RRs) or mean differences with 95% CI for dichotomous outcomes and continuous outcomes, respectively. Meta-analysis was performed using random-effects models. A prespecified analysis stratified by type of diet and quality assessment was performed to explore potential reasons for interstudy variation. Heterogeneity was assessed using I 2 statistics. Small study effects were examined for using funnel plots. Analyses were conducted using RevMan version 5.3. Pooled estimation of birth weight in the study and control arms, both overall and according to the specific diet intervention, was performed using Stata 14.0.

Quality Assessment

Methodological quality and bias assessment was completed by two reviewers. Risk of bias was assessed using the Cochrane Collaboration tool, which rates seven items as being high, low, or unclear for risk of bias ( 21 ). These items included random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective outcome reporting, and other potential sources of bias ( 21 ). A sensitivity analysis was performed excluding articles with relevant weaknesses in trial design or execution.

The overall quality of the evidence was also assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group guidelines ( 21 ). GRADE was assessed for all primary and secondary outcomes, both maternal and neonatal, but without subgroup analysis per different dietary intervention for each outcome measure.

We screened 2,269 records for potential inclusion, and 126 articles were reviewed in full ( Supplementary Fig. 1 ). Eighteen studies ( 12 – 14 , 22 – 36 ) were included in the meta-analysis with a total of 1,151 pregnant women with GDM.

Study Characteristics

The types of modified dietary intervention included low-GI ( n = 4), Dietary Approaches to Stop Hypertension (DASH) ( n = 3), low-carbohydrate ( n = 3), fat-modification ( n = 2), soy protein–enrichment ( n = 2), energy-restriction ( n = 1), high-fiber ( n = 1), and ethnic diets (i.e., foods commonly consumed according to participant’s ethnicity) ( n = 1) and behavioral intervention ( n = 1). Details of the study characteristics are included in Table 1 . Most trials were single centered and had small sample sizes (range 12–150). Only two trials (one each from Spain and Australia) included over 100 participants, nine had 50–100 participants, and seven studies had fewer than 50 participants. They were performed in North America, Europe, or Australasia and all had a duration of at least 2 weeks. The ethnicity of participants was reported in seven studies ( 12 , 13 , 26 , 29 , 31 , 32 , 34 ).

Characteristics of studies included

Unless otherwise stated, the units are kcal/day for energy, % for carbohydrate, protein, and fat. OGTT, oral glucose tolerance test.

*Reported actual dietary intake. When not reported, prescribed dietary intake is reported.

†Intervention is defined as dietary intervention different from the usual dietary intervention used in the control group.

‡Indicates prescribed diet.

§The control and intervention groups were reversed for the purpose of meta-analysis so it could be included in the low-carbohydrate group.

Most studies assessed individual dietary adherence using food diaries ( 13 , 23 – 36 ). Although most studies did report an overall difference in dietary composition between the intervention diet and control diet, few studies reported a detailed assessment of dietary adherence. Only five studies used a formal measure of adherence ( 24 , 25 , 29 , 33 , 34 ), and four of them reported data ( 25 , 29 , 33 , 34 ). Adherence ranged from 20% to 76% in the control groups and 60% to 80% in the intervention groups.

Participant Characteristics

When baseline characteristic data were pooled, women in the intervention group were older than women in the control group (pooled mean difference 0.60 years [95% CI 0.06, 1.14]) and had higher postprandial glucose (5.47 [0.86, 10.08]), most influenced by the DASH and ethnic diet studies. There was no overall significant difference between the intervention and control groups for BMI, gestational age at enrollment, fasting glucose, HbA 1c , or HOMA-IR.

Maternal Glycemic Outcomes for All Modified Dietary Interventions

Pooled risk ratios in 15 studies involving 1,023 women demonstrated a lower need for medication (RR 0.65 [95% CI 0.47, 0.88]; I 2 = 55) ( Table 2 ). Thirteen studies ( n = 662 women) reported fasting glucose levels, nine ( n = 475) reported combined postprandial glucose measures, and three ( n = 175) reported post-breakfast glucose measures. Pooled analysis demonstrated a larger decrease in fasting, combined postprandial, and post-breakfast glucose levels in modified dietary interventions (mean −4.07 mg/dL [95% CI −7.58, −0.57], I 2 = 86, P = 0.02; −7.78 mg/dL [−12.27, −3.29], I 2 = 63, P = 0.0007; and −4.76 mg/dL [−9.13, −0.38], I 2 = 34, P = 0.03, respectively) compared with control group. There were no significant differences in change in HbA 1c (seven studies), HOMA-IR (four studies), or in post-lunch or -dinner glucose levels (two studies).

Pooled analyses of primary maternal glycemic and infant birth weight outcomes

Neonatal Birth Weight Outcomes for All Diets

Pooled mean birth weight was 3,266.65 g (95% CI 3,172.15, 3,361.16) in the modified dietary intervention versus 3,449.88 g (3,304.34, 3,595.42) in the control group. Pooled analysis of all 16 modified dietary interventions including 841 participants demonstrated lower birth weight (mean −170.62 g [95% CI −333.64, −7.60], I 2 = 88; P = 0.04) and less macrosomia (RR 0.49 [95% CI 0.27, 0.88], I 2 = 11; P = 0.02) compared with conventional dietary advice ( Table 2 and Fig. 1 ). There was no significant difference in the risk of large-for-gestational-age newborns in modified dietary interventions as compared with control diets (RR 0.96 [95% CI 0.63, 1.46], I 2 = 0; P = 0.85).

Forest plot of birth weight for modified dietary interventions compared with control diets in women with GDM. Reference citations for studies can be found in Table 1 . CHO, carbohydrate; IV, inverse variance.

Subgroup Meta-analysis by Types of Dietary Interventions

Pooled analysis of low-GI diets showed a larger decrease in fasting ( 26 , 29 , 30 ), postprandial, and post-breakfast glucose compared with control diets ( 26 , 30 ) ( Table 2 ). However, the pooled analysis of the DASH diet showed significant favorable modifications in several outcomes, including change in fasting ( 22 , 36 ) and postprandial glucose ( 22 ), HOMA-IR ( 35 ), HbA 1c ( 22 ), medication need ( 22 , 23 , 36 ), infant birth weight ( 23 , 36 ), and macrosomia ( 23 , 36 ) ( Tables 2 and 3 ). Last, pooled analysis of the soy protein–enriched diet demonstrated a significant decrease in medication use and birth weight ( 14 , 27 ) ( Tables 2 and 3 ). One soy–protein intervention ( n = 68 participants) described significantly lower HOMA-IR ( 27 ) ( Table 2 ).

Sensitivity analysis of primary maternal glycemic and infant birth weight outcomes

Behavioral (one study) and ethnic-specific modified dietary interventions (one study) were included. The behavioral change dietary intervention reported significant differences in change in postprandial glucose and in HbA 1c ( Table 2 ) ( 24 ). The ethnic diet study demonstrated a significantly larger decrease in fasting and postprandial glucose ( Table 2 ) ( 34 ). Fat-modification, low-carbohydrate, and energy-restriction diets were not associated with a significant difference in our primary outcomes in the stratified analysis.

Secondary Outcomes

Weight gain from inclusion was lower for low-carbohydrate diets and cesarean birth for DASH diets ( Supplementary Table 2 ). Specific diet interventions did not show significant between-group differences in maternal gestational weight gain throughout pregnancy, preeclampsia/eclampsia, neonatal hypoglycemia as defined by the authors, preterm birth, neonatal intensive care unit admission, or small-for-gestational-age newborns ( Supplementary Tables 2 and 3 ).

Sensitivity Analysis of Primary Outcomes

Sensitivity analysis was performed to explore reasons for heterogeneity and to assess outcomes when studies with methodological concerns were removed. We were unable to include four studies ( 22 , 23 , 34 , 36 ), including all the DASH diet studies, where clarification of certain aspects of the results could not be obtained, even after a direct approach to the authors. The authors of the ethnic diet study responded to queries but did not provide the required information regarding gestational age at randomization ( 34 ). After these studies are removed, the changes in postprandial glucose (mean −5.90 mg/dL [95% CI −7.93, −3.88], I 2 = 0; P = 0.0001), post-breakfast glucose levels (−4.76 mg/dL [−9.13, −0.38], I 2 = 34; P = 0.03), and birth weight (−74.88 g [−144.86, −4.90], I 2 = 1; P = 0.04) remained significant when all diets were combined ( Table 3 ). Furthermore, the heterogeneity in most primary outcomes decreased after removal of these four studies.

When dietary subgroups were assessed, low-GI diets had significant differences in changes in fasting (mean −5.33 mg/dL [95% CI −6.91, −3.76]) ( 26 , 29 , 30 ), postprandial (−7.08 mg/dL [−12.07, −2.08]) ( 26 , 30 ), and post-breakfast (−8.6 mg/dL [−14.11, −3.09]) glucose ( 26 , 30 ). The soy protein–enriched diet had differences in change of HOMA-IR (mean −2.00 [95% CI −3.17, −0.83]) ( 27 ), required less medication use (RR 0.44 [95% CI 0.21, 0.91]), and had a lower birth weight (mean −184.67 g [95% CI −319.35, −49.98]) ( 14 , 27 ). The behavior modification diet had significant differences in change in postprandial glucose (mean −6.90 mg/dL [95% CI −9.85, −3.95]) and in HbA 1c (−0.19% [−0.26, −0.12]) ( 24 ) ( Table 3 ).

Assessment of Bias and Quality of the Evidence

None of the included studies were assessed as having a low risk of bias in all seven items of the Cochrane Collaboration tool ( Supplementary Fig. 2 ). Most studies were high risk for blinding of participants and personnel and for other sources of bias ( Supplementary Fig. 3 ). Studies scored high risk for other sources of bias for concerns such as baseline differences and industry funding. Most studies had an unclear risk of bias for selective outcome reporting and very few had registered protocols ( Supplementary Fig. 3 ).

GRADE assessment for the outcomes of interest reveals overall low to very low quality of evidence ( Supplementary Table 4 ). Considerations to downgrade quality of evidence involved the entire spectrum, including limitations in the study design, inconsistency in study results, and indirectness and imprecision in effect estimates.

Evaluation for Small Study Effect

Funnel plots of means and RRs of the primary outcomes for the main analysis are shown in Supplementary Figs. 4 and 5 and for the sensitivity analysis in Supplementary Figs. 6 and 7 . Overall, funnel plot asymmetry improves with the sensitivity analysis compared with the main analysis for neonatal birth weight outcomes.

In this meta-analysis, we pooled results from 18 studies including 1,151 women with a variety of modified dietary interventions. Remarkably, this is the first meta-analysis with a comprehensive analysis on maternal glucose parameters. Despite the heterogeneity between studies, we found a moderate effect of dietary interventions on maternal glycemic outcomes, including changes in fasting, post-breakfast, and postprandial glucose levels and need for medication treatment, and on neonatal birth weight. After removal of four studies with methodological concerns, we saw an attenuation of the treatment effect. Nonetheless, the change in post-breakfast and postprandial glucose levels and lowering of infant birth weight remained significant. Given the inconsistencies between the main and sensitivity analyses, we consider that conclusions should be drawn from the latter. These data suggest that dietary interventions modified above and beyond usual dietary advice for GDM have the potential to offer better maternal glycemic control and infant birth weight outcomes. However, the quality of evidence was judged as low to very low due to the limitations in the design of included studies, the inconsistency between their results, and the imprecision in their effect estimates.

Previous systematic reviews have focused on the easier-to-quantify outcomes, such as the decision to start additional pharmacotherapy and glucose-related variables at follow-up, but did not address change from baseline ( 15 – 17 ). The most recently published Cochrane systematic review by Han et al. ( 17 ) did not find any clear evidence of benefit other than a possible reduction in cesarean section associated with DASH diet. The very high-carbohydrate intake (∼400 g/day) and 12 servings of fruit and vegetables in the DASH diet ( 22 , 23 , 36 ) limit its clinical applicability and generalizability to women from lower socioeconomic, inner city backgrounds in Western countries. The Cochrane review shared one of our primary outcomes, large for gestational age ( 17 ). Neither meta-analysis detected a significant difference in risk of large for gestational age because the trials with a larger effect on birth weight (the three DASH studies) did not report on large for gestational age.

Our findings regarding pooled analysis of low-GI dietary interventions are broadly consistent with those of Viana et al. ( 16 ) and Wei et al. ( 15 ). Viana et al. ( 16 ) noted decreased birth weight and insulin use based on four studies of low-GI diet among 257 women (mean difference −161.9 g [95% CI −246.4, −77.4] and RR 0.767 [95% CI 0.597, 0.986], respectively). Wei et al. ( 15 ) also reported decreased risk of macrosomia with a low-GI diet in five studies of 302 women (RR 0.27 [95% CI 0.10, 0.71]). In our analyses of four studies in a comparable number of participants ( n = 276), we found the same direction of these effect estimates, without significant between-group differences. This is most likely due to the different studies included. For example, we were unable to obtain effect estimates stratified by type of diabetes in the study by Perichart-Perera et al. (which included women with type 2 diabetes) and therefore did not include this study ( 37 ). An important difference between our analyses and that of Wei et al. ( 15 ) is that they included DASH diet as a low-GI dietary subtype. We also included a recent study by Ma et al. ( 30 ) not included by the previous reviews.

Our sensitivity analyses highlighted concerns regarding some studies included in previous reviews. Notably, after removal of the studies with the most substantial methodological concerns in the sensitivity analysis, differences in the change in fasting plasma glucose were no longer significant. Although differences in the change in postprandial glucose and birth weight persisted, they were attenuated.

This review highlights limitations of the current literature examining dietary interventions in GDM. Most studies are too small to demonstrate significant differences in our primary outcomes. Seven studies had fewer than 50 participants and only two had more than 100 participants ( n = 125 and 150). The short duration of many dietary interventions and the late gestational age at which they were started ( 38 ) may also have limited their impact on glycemic and birth weight outcomes. Furthermore, we cannot conclude if the improvements in maternal glycemia and infant birth weight are due to reduced energy intake, improved nutrient quality, or specific changes in types of carbohydrate and/or protein.

We have not addressed the indirect modifications of nutrients. For example, reducing intake of dietary carbohydrates to decrease postprandial glucose may be compensated by a higher consumption of fat potentially leading to adverse effects on maternal insulin resistance and fetal body composition. Beneficial or adverse effects of other nutrients such as n-3 long-chain polyunsaturated fatty acid, vitamin D, iron, and selenium cannot be ruled out.

Our study has important strengths and weakness. To our knowledge, ours is the first systematic review of dietary interventions in GDM comprehensively examining the impact of diet on maternal glycemic outcomes assessing the change in fasting and postprandial glucose, HbA 1c , and HOMA-IR from baseline. This is especially important given that groups were not well balanced at baseline. Our review also benefits from the rigorous methodology used as well as the scientific, nutritional, and clinical expertise from an international interdisciplinary panel. However, it also has limitations. Baseline differences between groups in postprandial glucose may have influenced glucose-related outcomes. Furthermore, three of the included trials were pilot studies and therefore not designed to find between-group differences ( 12 , 26 , 34 ). The low number of studies reporting on adherence clearly illustrates that the quality of the evidence is far from ideal. The heterogeneity of the dietary interventions even within a specific type (varied macronutrient ratios, unknown micronutrient intake, and short length of some dietary interventions) and baseline characteristics of women included (such as prepregnancy BMI or ethnicity) may have also affected our pooled results. It should also be noted that the relatively small numbers of study participants limit between-diet comparisons. Last, we were unable to resolve queries regarding potential concerns for sources of bias because of lack of author response to our queries. We have addressed this by excluding these studies in the sensitivity analysis.

Modified dietary interventions favorably influenced outcomes related to maternal glycemia and birth weight. This indicates that there is room for improvement in usual dietary advice for women with GDM. Although the quality of the evidence in the scientific literature is low, our review highlights the key role of nutrition in the management of GDM and the potential for improvement if better recommendations based on adequately powered high-quality studies were developed. Given the prevalence of GDM, new studies designed to evaluate potential dietary interventions for these women should be based in larger study groups with appropriate statistical power. As most women with GDM are entering pregnancy with a high BMI, evidence-based recommendations regarding both dietary components and total energy intake are particularly important for overweight and obese women. The evaluation of nutrient quality, in addition to their quantity, as well as dietary patterns such as Mediterranean diet ( 39 ) would also be relevant. In particular, there is an urgent need for well-designed dietary intervention studies in the low- and middle-income countries where the global health consequences of GDM are greatest.

H.R.M. and R.C. contributed equally to this work.

See accompanying commentary, p. 1343 .

See accompanying articles, pp. 1337 , 1339 , 1362 , 1370 , 1378 , 1385 , 1391 , and e111 .

Funding. H.R.M. was funded by the U.K. National Institute for Health Research (CDF 2013-06-035). This work was conducted by an expert group of the European branch of the International Life Sciences Institute (ISLI Europe). This publication was coordinated by the ISLI Europe Early Nutrition and Long-Term Health and the Obesity and Diabetes task forces. Industry members of these task forces are listed on the ILSI Europe website at www.ilsi.eu . Experts are not paid for the time spent on this work; however, the nonindustry members within the expert group were offered support for travel and accommodation costs from the Early Nutrition and Long-Term Health and the Obesity and Diabetes task forces to attend meetings to discuss the manuscript and a small compensatory sum (honoraria) with the option to decline. The expert group carried out the work, i.e. collecting and analyzing data and information and writing the scientific paper, separate to other activities of the task forces. The research reported is the result of a scientific evaluation in line with ILSI Europe’s framework to provide a precompetitive setting for public-private partnership. ILSI Europe facilitated scientific meetings and coordinated the overall project management and administrative tasks relating to the completion of this work.

The opinions expressed herein and the conclusions of this publication are those of the authors and do not necessarily represent the views of ILSI Europe nor those of its member companies. For further information about ILSI Europe, please email [email protected] or call +32 2 771 00 14.

Duality of Interest. E.M.v.d.B. works part-time for Nutricia Research. E.C.-G. works full-time for Nestec. R.R. works full-time for Abbott Nutrition. No potential conflicts of interest relevant to this article were reported.

Author Contributions. J.M.Y. contributed to data extraction, statistical analyses, and writing the first draft manuscript. J.E.K. contributed to data extraction and writing the first draft summary tables. M.B. and A.G.-P. contributed to literature extraction, statistics, and manuscript revision. E.H. contributed to data extraction and GRADE assessments. I.S. and I.G. contributed to statistics and manuscript revision. E.M.v.d.B., E.C.-G., S.H., and S.F.O. contributed to concept and design, data extraction, and manuscript review. M.H. contributed to concept and design and draft manuscript evaluation. K.L. contributed to concept and design, data extraction, and critical review for intellectual content. L.P. contributed to concept and design and manuscript review. R.R., P.R., and H.R.M. contributed to concept and design, data extraction, and revising the draft manuscript. L.v.L. contributed to data extraction and draft summary tables. B.S. contributed to data extraction and critical review for intellectual content. R.C. contributed to literature extraction, statistical analyses, and revising the draft manuscript. R.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this work were presented at the Diabetes UK National Diabetes in Pregnancy Conference, Leeds, U.K., 14 November 2017, and the XXIX National Congress of the Spanish Society of Diabetes, Oviedo, Spain, 18–20 April 2018.

Supplementary data

Email alerts.

• Gestational Diabetes Mellitus: Does One Size Fit All? A Challenge to Uniform Worldwide Diagnostic Thresholds
• A Tailored Letter Based on Electronic Health Record Data Improves Gestational Weight Gain Among Women With Gestational Diabetes Mellitus: The Gestational Diabetes’ Effects on Moms (GEM) Cluster-Randomized Controlled Trial
• Day-and-Night Closed-Loop Insulin Delivery in a Broad Population of Pregnant Women With Type 1 Diabetes: A Randomized Controlled Crossover Trial
• Gestational Diabetes Mellitus and Renal Function: A Prospective Study With 9- to 16-Year Follow-up After Pregnancy
• Neonatal Hypoglycemia Following Diet-Controlled and Insulin-Treated Gestational Diabetes Mellitus
• The Human Placenta in Diabetes and Obesity: Friend or Foe? The 2017 Norbert Freinkel Award Lecture
• Nutrition Therapy in Gestational Diabetes Mellitus: Time to Move Forward
• Gestational Diabetes Mellitus: Is It Time to Reconsider the Diagnostic Criteria?
• The Sensitivity and Specificity of the Glucose Challenge Test in a Universal Two-Step Screening Strategy for Gestational Diabetes Mellitus Using the 2013 World Health Organization Criteria
• In This Issue of Diabetes Care
• Online ISSN 1935-5548
• Print ISSN 0149-5992
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Gestational diabetes...

Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis

• Related content
• Peer review
• Wenrui Ye , doctoral student 1 2 ,
• Cong Luo , doctoral student 3 ,
• Jing Huang , assistant professor 4 5 ,
• Chenglong Li , doctoral student 1 ,
• Zhixiong Liu , professor 1 2 ,
• 1 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 2 Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
• 3 Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 4 National Clinical Research Centre for Mental Disorders, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• 5 Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• Correspondence to: F Liu liufangkun{at}csu.edu.cn
• Accepted 18 April 2022

Objective To investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.

Design Systematic review and meta-analysis.

Data sources Web of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.

Review methods Cohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.

Results 156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.

Conclusions When adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

Review registration PROSPERO CRD42021265837.

• Download figure
• Open in new tab
• Download powerpoint

Introduction

Gestational diabetes mellitus is a common chronic disease in pregnancy that impairs the health of several million women worldwide. 1 2 Formally recognised by O’Sullivan and Mahan in 1964, 3 gestational diabetes mellitus is defined as hyperglycaemia first detected during pregnancy. 4 With the incidence of obesity worldwide reaching epidemic levels, the number of pregnant women diagnosed as having gestational diabetes mellitus is growing, and these women have an increased risk of a range of complications of pregnancy. 5 Quantification of the risk or odds of possible adverse outcomes of pregnancy is needed for prevention, risk assessment, and patient education.

In 2008, the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study recruited a large multinational cohort and clarified the risks of adverse outcomes associated with hyperglycaemia. The findings of the study showed that maternal hyperglycaemia independently increased the risk of preterm delivery, caesarean delivery, infants born large for gestational age, admission to a neonatal intensive care unit, neonatal hypoglycaemia, and hyperbilirubinaemia. 6 The obstetric risks associated with diabetes, such as pregnancy induced hypertension, macrosomia, congenital malformations, and neonatal hypoglycaemia, have been reported in several large scale studies. 7 8 9 10 11 12 The HAPO study did not adjust for some confounders, however, such as maternal body mass index, and did not report on stillbirths and neonatal respiratory distress syndrome, raising uncertainty about these outcomes. Other important pregnancy outcomes, such as preterm delivery, neonatal death, and low Apgar score in gestational diabetes mellitus, were poorly reported. No comprehensive study has assessed the relation between gestational diabetes mellitus and various maternal and fetal adverse outcomes after adjustment for confounders. Also, some cohort studies were restricted to specific clinical centres and regions, limiting their generalisation to more diverse populations.

By collating the available evidence, we conducted a systematic review and meta-analysis to quantify the short term outcomes in pregnancies complicated by gestational diabetes mellitus. We evaluated adjusted associations between gestational diabetes mellitus and various adverse outcomes of pregnancy.

This meta-analysis was conducted according to the recommendations of Cochrane Systematic Reviews, and our findings are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (table S16). The study was prospectively registered in the international database of prospectively registered systematic reviews (PROSPERO CRD42021265837).

Search strategy and selection criteria

We searched the electronic databases PubMed, Web of Science, Medline, and the Cochrane Database of Systematic Reviews with the keywords: “pregnan*,” “gestatio*” or “matern*” together with “diabete*,” “hyperglycaemia,” “insulin,” “glucose,” or “glucose tolerance test*” to represent the exposed populations, and combined them with terms related to outcomes, such as “pregnan* outcome*,” “obstetric* complicat*,” “pregnan* disorder*,” “obstetric* outcome*,” “haemorrhage,” “induc*,” “instrumental,” “caesarean section,” “dystocia,” “hypertensi*,” “eclampsia,” “premature rupture of membrane,” “PROM,” “preter*,” “macrosomia,” and “malformation,” as well as some abbreviated diagnostic criteria, such as “IADPSG,” “DIPSI,” and “ADIPS” (table S1). The search strategy was appropriately translated for the other databases. We included observational cohort studies and control arms of trials, conducted after 1990, that strictly defined non-gestational diabetes mellitus (control) and gestational diabetes mellitus (exposed) populations and had definite diagnostic criteria for gestational diabetes mellitus (table S2) and various adverse outcomes of pregnancy.

Exclusion criteria were: studies published in languages other than English; studies with no diagnostic criteria for gestational diabetes mellitus (eg, self-reported gestational diabetes mellitus, gestational diabetes mellitus identified by codes from the International Classification of Diseases or questionnaires); studies published after 1990 that recorded pregnancy outcomes before 1990; studies of specific populations (eg, only pregnant women aged 30-34 years, 13 only twin pregnancies 14 15 16 ); studies with a sample size <300, because we postulated that these studies might not be adequate to detect outcomes within each group; and studies published in the form of an abstract, letter, or case report.

We also manually retrieved reference lists of relevant reviews or meta-analyses. Three reviewers (WY, CL, and JH) independently searched and assessed the literature for inclusion in our meta-analysis. The reviewers screened the titles and abstracts to exclude ineligible studies. The full texts of relevant records were then retrieved and assessed. Any discrepancies were resolved after discussion with another author (FL).

Data extraction

Three independent researchers (WY, CL, and JH) extracted data from the included studies with a predesigned form. If the data were not presented, we contacted the corresponding authors to request access to the data. We extracted data from the most recent study or the one with the largest sample size when a cohort was reported twice or more. Sociodemographic and clinical data were extracted based on: year of publication, location of the study (country and continent), design of the study (prospective or retrospective cohort), screening method and diagnostic criteria for gestational diabetes mellitus, adjustment for conventional prognostic factors (defined as maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension), and the proportion of patients with gestational diabetes mellitus who were receiving insulin. For studies that adopted various diagnostic criteria for gestational diabetes mellitus, we extracted the most recent or most widely accepted one for subsequent analysis. For studies adopting multivariate logistic regression for adjustment of confounders, we extracted adjusted odds ratios and synthesised them in subsequent analyses. For unadjusted studies, we calculated risk ratios and 95% confidence intervals based on the extracted data.

Studies of women with gestational diabetes mellitus that evaluated the risk or odds of maternal or neonatal complications were included. We assessed the maternal outcomes pre-eclampsia, induction of labour, instrumental delivery, caesarean section, shoulder dystocia, premature rupture of membrane, and postpartum haemorrhage. Fetal or neonatal outcomes assessed were stillbirth, neonatal death, congenital malformation, preterm birth, macrosomia, low birth weight, large for gestational age, small for gestational age, neonatal hypoglycaemia, neonatal jaundice, respiratory distress syndrome, low Apgar score, and admission to the neonatal intensive care unit. Table S3 provides detailed definitions of these adverse outcomes of pregnancy.

Risk-of-bias assessment

A modified Newcastle-Ottawa scale was used to assess the methodological quality of the selection, comparability, and outcome of the included studies (table S4). Three independent reviewers (WY, CL, and JH) performed the quality assessment and scored the studies for adherence to the prespecified criteria. A study that scored one for selection or outcome, or zero for any of the three domains, was considered to have a high risk of bias. Studies that scored two or three for selection, one for comparability, and two for outcome were regarded as having a medium risk of bias. Studies that scored four for selection, two for comparability, and three for outcome were considered to have a low risk of bias. A lower risk of bias denotes higher quality.

Data synthesis and analysis

Pregnant women were divided into two groups (gestational diabetes mellitus and non-gestational diabetes mellitus) based on the diagnostic criteria in each study. Studies were considered adjusted if they adjusted for at least one of seven confounding factors (maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension). For each adjusted study, we transformed the odds ratio estimate and its corresponding standard error to natural logarithms to stabilise the variance and normalise their distributions. Summary odds ratio estimates and their 95% confidence intervals were estimated by a random effects model with the inverse variance method. We reported the results as odds ratio with 95% confidence intervals to reflect the uncertainty of point estimates. Unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy were quantified and summarised (table S6 and table S14). Thereafter, heterogeneity across the studies was evaluated with the τ 2 statistics and Cochran’s Q test. 17 18 Cochran’s Q test assessed interactions between subgroups. 18

We performed preplanned subgroup analyses for factors that could potentially affect gestational diabetes mellitus or adverse outcomes of pregnancy: country status (developing or developed country according to the International Monetary Fund ( www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm ), risk of bias (low, medium, or high), screening method (universal one step, universal glucose challenge test, or selective screening based on risk factors), diagnostic criteria for gestational diabetes mellitus (World Health Organization 1999, Carpenter-Coustan criteria, International Association of Diabetes and Pregnancy Study Groups (IADPSG), or other), and control for body mass index. We assessed small study effects with funnel plots by plotting the natural logarithm of the odds ratios against the inverse of the standard errors, and asymmetry was assessed with Egger’s test. 19 A meta-regression model was used to investigate the associations between study effect size and proportion of patients who received insulin in the gestational diabetes mellitus population. Next, we performed sensitivity analyses by omitting each study individually and recalculating the pooled effect size estimates for the remaining studies to assess the effect of individual studies on the pooled results. All analyses were performed with R language (version 4.1.2, www.r-project.org ) and meta package (version 5.1-0). We adopted the treatment arm continuity correction to deal with a zero cell count 20 and the Hartung-Knapp adjustment for random effects meta models. 21 22

Patient and public involvement

The experience in residency training in the department of obstetrics and the concerns about the association between gestational diabetes mellitus and health outcomes inspired the author team to perform this study. We also asked advice from the obstetrician and patients with gestational diabetes mellitus about which outcomes could be included. The covid-19 restrictions meant that we sought opinions from only a limited number of patients in outpatient settings.

Characteristics of included studies

Of the 44 993 studies identified, 156 studies, 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 involving 7 506 061 pregnancies, were eligible for the analysis of adverse outcomes in pregnancy ( fig 1 ). Of the 156 primary studies, 133 (85.3%) reported maternal outcomes and 151 (96.8%) reported neonatal outcomes. Most studies were conducted in Asia (39.5%), Europe (25.5%), and North America (15.4%). Eighty four (53.8%) studies were performed in developed countries. Based on the Newcastle-Ottawa scale, 50 (32.1%) of the 156 included studies showed a low or medium risk of bias and 106 (67.9%) had a high risk of bias. Patients in 35 (22.4%) of the 156 studies never used insulin during the course of the disease and 63 studies (40.4%) reported treatment with insulin in different proportions of patients. The remaining 58 studies did not report information about the use of insulin. Table 1 summarises the characteristics of the study population, including continent or region, country, screening methods, and diagnostic criteria for the included studies. Table S5 lists the key excluded studies.

Search and selection of studies for inclusion

Characteristics of study population

• View inline

Associations between gestational diabetes mellitus and adverse outcomes of pregnancy

Based on the use of insulin in each study, we classified the studies into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. We reported odds ratios with 95% confidence intervals after controlling for at least minimal confounding factors. In studies with no insulin use, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and an infant born large for gestational age (1.57, 1.25 to 1.97) ( fig 2 and fig S1). In studies with insulin use, adjusted for confounders, the odds of an infant born large for gestational age (odds ratio 1.61, 95% confidence interval 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31) were higher in women with than in those without gestational diabetes mellitus ( fig 3) . In studies that did not report the use of insulin, women with gestational diabetes mellitus had increased odds ratio for pre-eclampsia (1.46, 1.21 to 1.78), induction of labour (1.88, 1.16 to 3.04), caesarean section (1.38, 1.20 to 1.58), premature rupture of membrane (1.13, 1.06 to 1.20), congenital malformation (1.18, 1.10 to 1.26), preterm delivery (1.51, 1.19 to 1.93), macrosomia (1.48, 1.13 to 1.95), neonatal hypoglycaemia (11.71, 7.49 to 18.30), and admission to the neonatal intensive care unit (2.28, 1.26 to 4.13) (figs S3 and S4). We found no clear evidence for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and infant born small for gestational age between women with and without gestational diabetes mellitus in all three subgroups ( fig 2, fig 3, and figs S1-S4). Table S6 shows the unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy.

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies in patients who never used insulin during the course of the disease (no insulin use). NA=not applicable

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies where different proportions of patients were treated with insulin (insulin use). NA=not applicable

Subgroup, meta-regression, and sensitivity analyses

Subgroup analyses, based on risk of bias, did not show significant heterogeneity between the subgroups of women with and without gestational diabetes mellitus for most adverse outcomes of pregnancy ( table 2 and table 3 ), except for admission to the neonatal intensive care unit in studies where insulin use was not reported (table S7). Significant differences between subgroups were reported for country status and macrosomia in studies with (P<0.001) and without (P=0.001) insulin use ( table 2 and table 3 ), and for macrosomia (P=0.02) and infants born large for gestational age (P<0.001) based on adjustment for body mass index in studies with insulin use (table S8). Screening methods contributed significantly to the heterogeneity between studies for caesarean section (P<0.001) and admission to the neonatal intensive care unit (P<0.001) in studies where insulin use was not reported (table S7). In most outcomes, the estimated odds were lower in studies that used universal one step screening than those that adopted the universal glucose challenge test or selective screening methods ( table 2 and table 3 ). Diagnostic criteria were not related to heterogeneity between the studies for all of the study subgroups (no insulin use, insulin use, insulin use not reported). The subgroup analysis was performed only for outcomes including ≥6 studies.

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with no insulin use

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with insulin use

We applied meta-regression models to evaluate the modification power of the proportion of patients with insulin use when sufficient data were available. Significant associations were found between effect size estimate and proportion of patients who had received insulin for the adverse outcomes caesarean section (estimate=0.0068, P=0.04) and preterm delivery (estimate=−0.0069, P=0.04) (table S9).

In sensitivity analyses, most pooled estimates were not significantly different when a study was omitted, suggesting that no one study had a large effect on the pooled estimate. The pooled estimate effect became significant (P=0.005) for low birth weight when the study of Lu et al 99 was omitted, however (fig S5). We found evidence of a small study effect only for caesarean section (Egger’s P=0.01, table S10). Figure S6 shows the funnel plots of the included studies for various adverse outcomes (≥10 studies).

Principal findings

We have provided quantitative estimates for the associations between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for confounding factors, through a systematic search and comprehensive meta-analysis. Compared with patients with normoglycaemia during pregnancy, patients with gestational diabetes mellitus had increased odds of caesarean section, preterm delivery, low one minute Apgar score, macrosomia, and an infant born large for gestational age in studies where insulin was not used. In studies with insulin use, patients with gestational diabetes mellitus had an increased odds of an infant born large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit. Our study was a comprehensive analysis, quantifying the adjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy. The study provides updated critical information on gestational diabetes mellitus and adverse outcomes of pregnancy and would facilitate counselling of women with gestational diabetes mellitus before delivery.

To examine the heterogeneity conferred by different severities of gestational diabetes mellitus, we categorised the studies by use of insulin. Insulin is considered the standard treatment for the management of gestational diabetes mellitus when adequate glucose levels are not achieved with nutrition and exercise. 179 Our meta-regression showed that the proportion of patients who had received insulin was significantly associated with the effect size estimate of adverse outcomes, including caesarean section (P=0.04) and preterm delivery (P=0.04). This finding might be the result of a positive linear association between glucose concentrations and adverse outcomes of pregnancy, as previously reported. 180 However, the proportion of patients who were receiving insulin indicates the percentage of patients with poor glycaemic control in the population and cannot reflect glycaemic control at the individual level.

Screening methods for gestational diabetes mellitus have changed over time, from the earliest selective screening (based on risk factors) to universal screening by the glucose challenge test or the oral glucose tolerance test, recommended by the US Preventive Services Task Force (2014) 181 and the American Diabetes Association (2020). 182 The diagnostic accuracy of these screening methods varied, contributing to heterogeneity in the analysis.

Several studies have tried to pool the effects of gestational diabetes mellitus on pregnancy outcomes, but most focused on one outcome, such as congenital malformations, 183 184 macrosomia, 185 186 or respiratory distress syndrome. 187 Our findings of increased odds of macrosomia in gestational diabetes mellitus in studies where insulin was not used, and respiratory distress syndrome in studies with insulin use, were similar to the results of previous meta-analyses. 188 189 The increased odds of neonatal respiratory distress syndrome, along with low Apgar scores, might be attributed to disruption of the integrity and composition of fetal pulmonary surfactant because gestational diabetes mellitus can delay the secretion of phosphatidylglycerol, an essential lipid component of surfactants. 190

Although we detected no significant association between gestational diabetes mellitus and mortality events, the observed increase in the odds of neonatal death (odds ratio 1.59 in studies that did not report the use of insulin) should be emphasised to obstetricians and pregnant women because its incidence was low (eg, 3.75% 87 ). The increased odds of neonatal death could result from several lethal complications, such as respiratory distress syndrome, neonatal hypoglycaemia (3.94-11.71-fold greater odds), and jaundice. These respiratory and metabolic disorders might increase the likelihood of admission to the neonatal intensive care unit.

For the maternal adverse outcomes, women with gestational diabetes mellitus had increased odds of pre-eclampsia, induction of labour, and caesarean section, consistent with findings in previous studies. 126 Our study identified a 1.24-1.46-fold greater odds of pre-eclampsia between patients with and without gestational diabetes mellitus, which was similar to previous results. 191

Strengths and limitations of the study

Our study included more studies than previous meta-analyses and covered a range of maternal and fetal outcomes, allowing more comprehensive comparisons among these outcomes based on the use of insulin and different subgroup analyses. The odds of adverse fetal outcomes, including respiratory distress syndrome (P=0.002), neonatal jaundice (P=0.05), and admission to the neonatal intensive care unit (P=0.005), were significantly increased in studies with insulin use, implicating their close relation with glycaemic control. The findings of this meta-analysis support the need for an improved understanding of the pathophysiology of gestational diabetes mellitus to inform the prediction of risk and for precautions to be taken to reduce adverse outcomes of pregnancy.

The study had some limitations. Firstly, adjustment for at least one confounder had limited power to deal with potential confounding effects. The set of adjustment factors was different across studies, however, and defining a broader set of multiple adjustment variables was difficult. This major concern should be looked at in future well designed prospective cohort studies, where important prognostic factors are controlled. Secondly, overt diabetes was not clearly defined until the IADPSG diagnostic criteria were proposed in 2010. Therefore, overt diabetes or pre-existing diabetes might have been included in the gestational diabetes mellitus groups if studies were conducted before 2010 or adopted earlier diagnostic criteria. Hence we cannot rule out that some adverse effects in newborns were related to prolonged maternal hyperglycaemia. Thirdly, we divided and analysed the subgroups based on insulin use because insulin is considered the standard treatment for the management of gestational diabetes mellitus and can reflect the level of glycaemic control. Accurately determining the degree of diabetic control in patients with gestational diabetes mellitus was difficult, however. Finally, a few pregnancy outcomes were not accurately defined in studies included in our analysis. Stillbirth, for example, was defined as death after the 20th or 28th week of pregnancy, based on different criteria, but some studies did not clearly state the definition of stillbirth used in their methods. Therefore, we considered stillbirth as an outcome based on the clinical diagnosis in the studies, which might have caused potential bias in the analysis.

Conclusions

We performed a meta-analysis of the association between gestational diabetes mellitus and adverse outcomes of pregnancy in more than seven million women. Gestational diabetes mellitus was significantly associated with a range of pregnancy complications when adjusted for confounders. Our findings contribute to a more comprehensive understanding of adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

What is already known on this topic

The incidence of gestational diabetes mellitus is gradually increasing and is associated with a range of complications for the mother and fetus or neonate

Pregnancy outcomes in gestational diabetes mellitus, such as neonatal death and low Apgar score, have not been considered in large cohort studies

Comprehensive systematic reviews and meta-analyses assessing the association between gestational diabetes mellitus and adverse pregnancy outcomes are lacking

What this study adds

This systematic review and meta-analysis showed that in studies where insulin was not used, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean delivery, preterm delivery, low one minute Apgar score, macrosomia, and an infant large for gestational age in the pregnancy outcomes

In studies with insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of an infant large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit

Future primary studies should routinely consider adjusting for a more complete set of prognostic factors

Ethics statements

Ethical approval.

Not required.

Data availability statement

Table S11 provides details of adjustment for core confounders. Supplementary data files contain all of the raw tabulated data for the systematic review (table S12). Tables S13-15 provide the raw data and R language codes used for the meta-analysis.

Contributors: WY and FL developed the initial idea for the study, designed the scope, planned the methodological approach, wrote the computer code and performed the meta-analysis. WY and CL coordinated the systematic review process, wrote the systematic review protocol, completed the PROSPERO registration, and extracted the data for further analysis. ZL coordinated the systematic review update. WY, JH, and FL defined the search strings, executed the search, exported the results, and removed duplicate records. WY, CL, ZL, and FL screened the abstracts and texts for the systematic review, extracted relevant data from the systematic review articles, and performed quality assessment. WY, ZL, and FL wrote the first draft of the manuscript and all authors contributed to critically revising the manuscript. ZL and FL are the study guarantors. ZL and FL are senior and corresponding authors who contributed equally to this study. All authors had full access to all the data in the study, and the corresponding authors had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The research was funded by the National Natural Science Foundation of China (grants 82001223 and 81901401), and the Natural Science Foundation for Young Scientist of Hunan Province, China (grant 2019JJ50952). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Natural Science Foundation of China and the Natural Science Foundation for Young Scientist of Hunan Province, China for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The dissemination plan targets a wide audience, including members of the public, patients, patient and public communities, health professionals, and experts in the specialty through various channels: written communication, events and conferences, networks, and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Saravanan P ,
• Diabetes in Pregnancy Working Group ,
• Maternal Medicine Clinical Study Group ,
• Royal College of Obstetricians and Gynaecologists, UK
• O’Sullivan JB ,
• Hartling L ,
• Dryden DM ,
• Guthrie A ,
• Vandermeer B ,
• McIntyre HD ,
• Catalano P ,
• Mathiesen ER ,
• Metzger BE ,
• HAPO Study Cooperative Research Group
• Persson M ,
• Balsells M ,
• García-Patterson A ,
• Simmonds M ,
• Murphy HR ,
• Roland JM ,
• East Anglia Study Group for Improving Pregnancy Outcomes in Women with Diabetes (EASIPOD)
• Magnuson A ,
• Simmons D ,
• Sumeksri P ,
• Wongyai S ,
• González González NL ,
• Bellart J ,
• Guillén MA ,
• Herranz L ,
• Barquiel B ,
• Hillman N ,
• Burgos MA ,
• Pallardo LF
• Higgins JP ,
• Thompson SG ,
• Davey Smith G ,
• Schneider M ,
• Sweeting MJ ,
• Sutton AJ ,
• Hartung J ,
• IntHout J ,
• Ioannidis JP ,
• Alberico S ,
• Montico M ,
• Barresi V ,
• Multicentre Study Group on Mode of Delivery in Friuli Venezia Giulia
• Alfadhli EM ,
• Anderberg E ,
• Ardawi MS ,
• Nasrat HA ,
• Al-Sagaaf HM ,
• Kenealy T ,
• Barakat MN ,
• Youssef RM ,
• Al-Lawati JA
• Ibrahim I ,
• Eltaher F ,
• Benhalima K ,
• Hanssens M ,
• Devlieger R ,
• Verhaeghe J ,
• Berggren EK ,
• Boggess KA ,
• Stuebe AM ,
• Jonsson Funk M
• Korucuoglu U ,
• Aksakal N ,
• Himmetoglu O
• Bodmer-Roy S ,
• Cousineau J ,
• Broekman BFP ,
• GUSTO study group
• Catalano PM ,
• Cruickshank JK ,
• Chanprapaph P ,
• Cheung NW ,
• Lopez-Rodo V ,
• Rodriguez-Vaca D ,
• Benchimol M ,
• Carbillon L ,
• Benbara A ,
• Pharisien I ,
• Scifres CM ,
• Gorban de Lapertosa S ,
• Salzberg S ,
• DPSG-SAD Group
• Zijlmans AB ,
• Rademaker D ,
• Djelmis J ,
• Mulliqi Kotori V ,
• Pavlić Renar I ,
• Ivanisevic M ,
• Oreskovic S
• Domanski G ,
• Ittermann T ,
• Donovan LE ,
• Edwards AL ,
• Torrejón MJ ,
• Ekeroma AJ ,
• Chandran GS ,
• McCowan L ,
• Eagleton C ,
• Erjavec K ,
• Poljičanin T ,
• Matijević R
• Ethridge JK Jr . ,
• Feleke BE ,
• Feleke TE ,
• Forsbach G ,
• Cantú-Diaz C ,
• Vázquez-Lara J ,
• Villanueva-Cuellar MA ,
• Alvarez y García C ,
• Rodríguez-Ramírez E
• Gonçalves E ,
• Gortazar L ,
• Flores-Le Roux JA ,
• Benaiges D ,
• Gruendhammer M ,
• Brezinka C ,
• Lechleitner M
• Hedderson MM ,
• Ferrara A ,
• Hillier TA ,
• Pedula KL ,
• Morris JM ,
• Hossein-Nezhad A ,
• Maghbooli Z ,
• Vassigh AR ,
• Massaro N ,
• Streckeisen S ,
• Ikenoue S ,
• Miyakoshi K ,
• Raghav SK ,
• Jensen DM ,
• Sørensen B ,
• Rich-Edwards JW ,
• Kreisman S ,
• Tildesley H
• Kachhwaha CP ,
• Kautzky-Willer A ,
• Bancher-Todesca D ,
• Weitgasser R ,
• Keikkala E ,
• Mustaniemi S ,
• Koivunen S ,
• Keshavarz M ,
• Babaee GR ,
• Moghadam HK ,
• Kgosidialwa O ,
• Carmody L ,
• Gunning P ,
• Kieffer EC ,
• Carman WJ ,
• Sanborn CZ ,
• Viljakainen M ,
• Männistö T ,
• Kachhawa G ,
• Laafira A ,
• Griffin CJ ,
• Johnson JA ,
• Lapolla A ,
• Dalfrà MG ,
• Ragazzi E ,
• De Cata AP ,
• Norwitz E ,
• Leybovitz-Haleluya N ,
• Wainstock T ,
• Hinkle SN ,
• Grantz KL ,
• Lopez-de-Andres A ,
• Carrasco-Garrido P ,
• Gil-de-Miguel A ,
• Hernandez-Barrera V ,
• Jiménez-García R
• Luengmettakul J ,
• Sunsaneevithayakul P ,
• Talungchit P
• Macaulay S ,
• Munthali RJ ,
• Dunger DB ,
• Makwana M ,
• Bhimwal RK ,
• El Mallah KO ,
• Kulaylat NA ,
• Melamed N ,
• Vandenberghe H ,
• Jensen RC ,
• Kibusi SM ,
• Munyogwa MJ ,
• Patient C ,
• Miailhe G ,
• Legardeur H ,
• Mandelbrot L
• Minsart AF ,
• N’guyen TS ,
• Ratsimandresy R ,
• Ali Hadji R
• Matsumoto T ,
• Morikawa M ,
• Sugiyama T ,
• Knights SJ ,
• Olayemi OO ,
• Mwanri AW ,
• Ramaiya K ,
• Abalkhail B ,
• Nguyen TH ,
• Nguyen CL ,
• Minh Pham N ,
• Nicolosi BF ,
• Vernini JM ,
• Kragelund Nielsen K ,
• Andersen GS ,
• Nybo Andersen AM
• Ogonowski J ,
• Miazgowski T ,
• Czeszyńska MB ,
• Kuczyńska M ,
• Miazgowski T
• Morrish DW ,
• O’Sullivan EP ,
• O’Reilly M ,
• Dennedy MC ,
• Gaffney G ,
• Atlantic DIP collaborators
• Ovesen PG ,
• Rasmussen S ,
• Kesmodel US
• Ozumba BC ,
• Premuzic V ,
• Zovak Pavic A ,
• Bevanda M ,
• Mihaljevic S ,
• Ramachandran A ,
• Snehalatha C ,
• Clementina M ,
• Sasikala R ,
• Redman LM ,
• LIFE-Moms Research Group
• Spanish Group for the Study of the Impact of Carpenter and Coustan GDM thresholds
• Bowker SL ,
• Montoro MN ,
• Lawrence JM
• Kamalanathan S ,
• Saldana TM ,
• Siega-Riz AM ,
• Savitz DA ,
• Thorp JM Jr .
• Savona-Ventura C ,
• Schwartz ML ,
• Lubarsky SL
• Segregur J ,
• Buković D ,
• Milinović D ,
• Cavkaytar S ,
• Shahbazian H ,
• Nouhjah S ,
• Shahbazian N ,
• McElduff A ,
• Sheffield JS ,
• Butler-Koster EL ,
• McIntire DD ,
• Saigusa Y ,
• Nakanishi S ,
• Templeton A ,
• Sirimarco MP ,
• Guerra HM ,
• Lisboa EG ,
• Sletner L ,
• Yajnik CS ,
• Soliman A ,
• Al Rifai H ,
• Soonthornpun S ,
• Soonthornpun K ,
• Aksonteing J ,
• Thamprasit A
• Srichumchit S ,
• Sugiyama MS ,
• Roseveare C ,
• Basilius K ,
• Madraisau S
• Hansen BB ,
• Mølsted-Pedersen L
• Caswell A ,
• Holliday E ,
• Petrović O ,
• Crnčević Orlić Ž ,
• Vambergue A ,
• Nuttens MC ,
• Goeusse P ,
• Biausque S ,
• van Hoorn J ,
• von Katterfeld B ,
• McNamara B ,
• Langridge AT
• Wahabi HA ,
• Esmaeil SA ,
• Alzeidan RA
• Esmaeil S ,
• Mamdouh H ,
• Wahlberg J ,
• Nyström L ,
• Persson B ,
• Arnqvist HJ
• Nankervis A ,
• Weijers RN ,
• Bekedam DJ ,
• Smulders YM
• Bleicher K ,
• Saunders LD ,
• Demianczuk NN
• Homer CSE ,
• Sullivan EA
• American Diabetes Association
• U.S. Preventive Services Task Force
• Tabrizi R ,
• Lankarani KB ,
• Leung-Pineda V ,
• Gronowski AM
• Bryson CL ,
• Ioannou GN ,
• Rulyak SJ ,
• Critchlow C

• Open access
• Published: 08 August 2022

A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally

• Sheila Pham 1 ,
• Kate Churruca 1 ,
• Louise A. Ellis 1 &
• Jeffrey Braithwaite 1  

BMC Pregnancy and Childbirth volume  22 , Article number:  627 ( 2022 ) Cite this article

4122 Accesses

9 Citations

5 Altmetric

Metrics details

Gestational diabetes mellitus (GDM) is a condition associated with pregnancy that engenders additional healthcare demand. A growing body of research includes empirical studies focused on pregnant women’s GDM healthcare experiences. The aim of this scoping review is to map findings, highlight gaps and investigate the way research has been conducted into the healthcare experiences of women with GDM.

A systematic search of primary research using a number of databases was conducted in September 2021. Studies were included if they had an explicit aim of focusing on GDM and included direct reporting of participants’ experiences of healthcare. Key data from each study was extracted into a purposely-designed form and synthesised using descriptive statistics and thematic analysis.

Fifty-seven articles were included in the analysis. The majority of studies used qualitative methodology, and did not have an explicit theoretical orientation. Most studies were conducted in urban areas of high-income countries and recruitment and research was almost fully conducted in clinical and other healthcare settings. Women found inadequate information a key challenge, and support from healthcare providers a critical factor. Experiences of prescribed diet, medication and monitoring greatly varied across settings. Additional costs associated with managing GDM was cited as a problem in some studies. Overall, women reported significant mental distress in relation to their experience of GDM.

Conclusions

This scoping review draws together reported healthcare experiences of pregnant women with GDM from around the world. Commonalities and differences in the global patient experience of GDM healthcare are identified.

Peer Review reports

Gestational diabetes mellitus (GDM) is defined as any degree of hyperglycaemia recognised for the first time during pregnancy, including type 2 diabetes mellitus diagnosed during pregnancy as well as true GDM which develops in pregnancy [ 1 ]. GDM is associated with a number of adverse maternal and neonatal outcomes, including increased birth weight and increased cord-blood serum C-peptide levels [ 2 ], as well as greater risk of future diabetes [ 3 ].

The global incidence and health burden of GDM is increasing [ 4 ] and the cost of healthcare relating to GDM significant. In 2019, the International Diabetes Federation estimated the annual global diabetes-related health expenditure, which includes GDM, reached USD$760 billion [ 4 ]. In China, for example, the annual societal economic burden of GDM is estimated to be ¥19.36 billion ($5.59 billion USD) [ 5 ].

GDM is estimated to affect 7–10% of all pregnancies worldwide, though the absence of a universal gold standard for screening means it is difficult to achieve an accurate estimation of prevalence [ 6 ], and the prevalence of GDM varies considerably depending on the data source used [ 7 ]. In Australia, for example, between 2000 and 01 and 2017-18, the rate of diagnosis for GDM tripled from 5.2 to 16.1% (3); furthermore, in 2017-18, there were around 53,700 hospitalisations for a birth event where gestational diabetes was recorded as the principal and/or additional diagnosis [ 8 ]. Important risk factors for GDM include being overweight/obese, advanced maternal age and having a family history of diabetes mellitus (DM), with all these risk factors dependent on foreign-born racial/ethnic minority status [ 9 ]. However, primarily directing research to understanding risk factors does not necessarily lead to better pregnancy care, particularly where diabetes is concerned, and developing better interventions requires consideration of women’s beliefs, behaviours and social environments [ 10 ].

To date there have been numerous systematic and scoping reviews focused on women’s experiences of GDM, which provide a comprehensive overview of numerous issues. However, gaps remain. In 2014, Nielsen et al. [ 11 ] reviewed qualitative and quantitative studies to investigate determinants and barriers to women’s use of GDM healthcare services, finding that although most women expressed commitment to following health professional advice to manage GDM, compliance with treatment was challenging. Their review also noted that only four out of the 58 included studies were conducted in low-income countries. In their follow-up review, Nielsen et al. specifically focused on research from low and middle income countries (LMIC) to examine barriers and facilitators for implementing programs and services for hyperglycaemia in pregnancy in those settings [ 12 ] and identified a range of factors such as women reporting treatment is “expensive, troublesome and difficult to follow”.

In 2014, Costi et al. [ 13 ] reviewed 22 qualitative studies on women’s experiences of diabetes and diabetes management in pregnancy, including both pre-existing diabetes and GDM. From their synthesis of study findings, they concluded that health professionals need to take a more whole-person approach when treating women with GDM, and that prescribed regimes need to be more accommodating [ 13 ]. Another 2014 review by Parsons et al. [ 14 ] conducted a narrative meta-synthesis of qualitative studies. Their 16 included studies focused on the experiences of women with GDM, including healthcare support and information, but the focus of their meta-synthesis was focused on perceptions of diabetes risk and views on future diabetes prevention.

In a systematic review of qualitative and survey studies from 2015, Van Ryswyck et al. [ 15 ] included 42 studies and had similar findings to Parsons et al. [ 14 ], also emphasising their findings regarding the emotional responses of women who have experienced GDM. Specifically, Van Ryswyck et al. [ 15 ] identified that women’s experiences ran the gamut of emotions from “very positive to difficult and confusing”, with a clear preference for non-judgmental and positively focused care. Most recently, the 2020 systematic review of qualitative studies by He et al. [ 16 ] synthesised findings from 10 studies to argue that understanding the experiences of women with GDM can aid health care professionals to better understand those under their care and to develop more feasible interventions to reduce the risk of DM. A further systematic review of qualitative studies by Craig et al. [ 17 ] focused on women’s psychosocial experiences of GDM diagnosis, one important aspect of healthcare experience, highlighting future directions for research into the psychosocial benefits and harms of a GDM diagnosis.

There has been insufficient consideration of epistemological assumptions and other aspects of research design which may affect how such studies are framed, which participants are included, how data is collected and subsequently what findings are spotlighted. While women’s experiences of GDM healthcare are often broadly included in reviews, they are not often the exclusive focus with healthcare experiences folded into accounts of living with GDM [ 11 ], healthcare service implementation [ 12 ], diabetes and pregnancy [ 13 ], understanding of future risk [ 14 ] and seeking postpartum care after GDM [ 15 ].

To address this gap, the aim of this review was to map the literature, identify gaps in knowledge and investigate the ways research has been conducted into GDM healthcare experiences. The research questions were:

When, where and how has knowledge been produced about women’s experiences of GDM healthcare?

What findings have been reported about women’s experience of GDM healthcare?

A scoping review was selected as the most appropriate method given our multiple aims relate to mapping the field of GDM healthcare experiences [ 18 ]. The reporting of this scoping review was guided by an adaptation of the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) reporting guidelines [ 19 ].

Search strategy

The search strategy was designed in consultation with a research librarian. The following databases were used: Scopus, PubMed, CINAHL, Web of Science, MEDLINE, Embase and Joanna Briggs Institute EBP. These databases were searched on 27 September 2021 by the first author using the keywords and MESH terms outlined in Table  1 . No limits were set on publication date, study design or country of origin. The reference lists of included articles were also examined to identify other potential articles (i.e. snowballing).

Study selection

References were downloaded into Endnote before being exported into the online systematic review platform Rayyan [ 20 ]. Titles and abstracts were first screened against inclusion criteria by the first author and uncertainties about article inclusion were referred to the second and third authors for a decision. A second reviewer independently screened a subset (5%) of titles and abstracts of studies for eligibility to ensure inclusion criteria were consistently applied. Studies were included if they reported primary (empirical) research in the English-language in a published peer-reviewed journal. Studies had to have an explicit aim of focusing on GDM and include direct reporting of participants’ experiences of healthcare. The experience of healthcare is here understood as being the patient experience of care occurring in formal clinical settings, including interactions with providers and other aspects of care prescribed by healthcare professionals. Exclusion criteria were reviews of any kind, research that was not empirical (e.g. personal accounts) and conference abstracts.

Data extraction and synthesis

Data from studies including authors, year published, study design, setting, sample size, recruitment site, stated theoretical approach, data collection method, languages and findings, were extracted into a custom template developed in Microsoft Excel. Findings were further summarised through an iterative coding process and used to develop a series of categories that broadly captured women’s experiences of GDM healthcare.

Search results

A total of 2856 articles were identified as potentially relevant to the research question from database searches. After removing duplicates ( n  = 811) and excluding non-relevant studies by screening titles and abstracts ( n  = 2045) and identifying an additional study through snowballing ( n  = 1), 112 articles were examined for inclusion through a full text assessment. Of these, 57 articles were included in this review, with 55 studies being excluded with reasons for exclusion documented. Figure  1 outlines the process of data gathering and Additional file: Appendix 1 for summarised study characteristics.

The process of data gathering

Publication dates

All of the included studies were published from 2005 onwards, except for one early study published in 1994 [ 21 ]. There has been an overall increase in the number of studies published each year to 2020 (see Fig.  2 ).

Included studies published over time

Research settings

For the vast majority of studies ( n  = 55, 91%), recruitment of women with GDM was conducted via hospitals, clinics and healthcare providers, with one of these studies also conducting additional recruitment via workplaces [ 22 ]. Electronic databases were used in two studies for recruitment, with one study using a national diabetes database in Australia [ 23 ] and another using electronic health data in the United States [ 24 ]. Two studies which targeted Indigenous populations relied on pre-existing relationships; a Canadian study gained entry to an Indigenous population by building on pre-existing relationships with the Mi’kmaq communities [ 25 ] and an Australian study which focused on Aboriginal populations relied on existing research networks [ 26 ]. Only one study recruited completely outside clinical, healthcare and research settings using advertisements and community notices in targeted areas of Atlanta, Georgia in the United States [ 27 ].

A handful of studies ( n  = 5, 9%) were based in countries classified as low- and lower middle-income; there were no countries considered ‘least developed’ [ 28 ]. For the most part, included studies were concentrated in a relatively small number of high-income countries, with the top six countries for research on women’s experiences of GDM healthcare being Australia ( n  = 11), Canada ( n  = 8), Sweden ( n  = 7), the United States ( n  = 6), the United Kingdom ( n  = 4) and China ( n  = 4). The remaining studies were spread across a number of countries, largely one study per setting: Austria [ 29 ], Brazil [ 30 ], Denmark [ 31 ], Ghana [ 32 ], India [ 33 ], Indonesia [ 34 ], Iran [ 35 , 36 ], Malaysia [ 37 ], New Zealand [ 38 , 39 ], Norway [ 40 ], Singapore [ 41 ], South Africa [ 42 , 43 ], Vietnam [ 44 ], Zimbabwe [ 45 ] (see Fig.  3 ).

Settings of included studies

Forty-eight of the studies (84%) were conducted with participants in urban areas and the remaining studies ( n  = 9) were conducted in regional and rural areas of Australia [ 26 , 46 ], Canada [ 25 , 47 , 48 , 49 ], China [ 50 ], Tamil Nadu in India [ 33 ], and the state of New York in the United States [ 51 ]. A number of studies were conducted by the same research team and published in multiple installments; these studies were conducted in Lund, Sweden (6 studies), southeastern China (4 studies) and Melbourne, Australia (4 studies).

Participants

The majority of studies specifically focused on women diagnosed with GDM as the sole target group, though two studies also interviewed comparative groups of women with different conditions such as DM [ 27 , 52 ]. Several studies targeted women as well as healthcare professionals, including nurses, clinicians, general practitioners, with data being compared between groups [ 26 , 27 , 32 , 36 , 41 , 46 , 47 , 53 , 54 ]. In one study it was noted how some participants had pre-existing medical conditions, such as hypertension and HIV, and that their co-morbidities directly contributed to their perspective on GDM [ 36 ].

Depending on the nature of the study design—whether qualitative, mixed methods or quantitative—the range of participants varied greatly, from a small number of interview and focus group participants ( n  = 8) [ 55 ] through to large datasets such as the open-ended responses on a cross-sectional survey ( n  = 393) [ 23 ]. While there was some stratification of participants based on individual factors, such as body mass index [ 56 ] as well as glycaemic targets set [ 38 ], the main categorisation made was often in relation to ethnicity in studies from countries such as Australia, Sweden and the United States, where the focus on ethnic differences was built into the design of studies. For example, this included directly comparing ethnic groups, such as Swedish-born versus African-born [ 57 ], or comparing groups of women by their ethnicity, namely Caucasian, Arabic and Chinese [ 58 ].

Study designs

The studies varied in how they understood, described and measured women’s experiences of GDM healthcare. Of the 57 included studies, 50 (88%) used qualitative study designs. Only four studies (7%) had quantitative designs and three (5%) employed mixed-methods [ 29 ]. The vast majority of studies ( n  = 49, 86%) were cross-sectional, with seven studies [ 21 , 51 , 56 , 59 , 60 , 61 , 62 ] interviewing the same women at multiple time points. In terms of methodologies used, all the qualitative studies featured various types of interviews and/or focus groups. These were largely conducted face-to-face or via telephone. Seven studies employed more than one qualitative method to collect data [ 36 , 43 , 47 , 55 , 63 , 64 , 65 ] and, in addition, three studies used mixed methods to collect data [ 29 , 41 , 46 ]. One study focused on First Nations women in Canada used a focused ethnographic approach [ 49 ], and another 2021 study focused on South Asian women in Australia using ethnography [ 54 ]. The quantitative studies comprised four survey studies using questionnaires [ 37 , 38 , 52 , 66 ].

Theoretical approaches

The majority of studies did not specify a theoretical approach ( n  = 31, 54%), and relied on general data analysis approaches such as thematic analysis. Where a theory was referred to, it was largely used as a guiding framework for study design and data collection, and data analysis where applicable (see Additional file: Appendix 1 ). The three most popular theoretical approaches were the Health Belief Model ( n  = 6), Grounded Theory ( n  = 3) and phenomenology ( n  = 8), with the last of these specifically including hermeneutic [ 67 ] and interpretative approaches [ 63 , 68 ]. Two of the studies that focused on Indigenous populations used culturally-sensitive qualitative methodologies designed to respect and recognise Indigenous worldviews, namely the Two-Eyed Seeing Approach [ 25 ] and the Kaupapa Māori methodology [ 39 ]. Another study [ 47 ] focused on an Indigenous population discussed qualitative research in general being the most “flexible and interpretive methodology” and how using open-ended interviewing creates a dialogue which recognises Indigenous oral traditions and knowledge.

Data collection

Studies varied in when they captured data during the pregnancy and postpartum periods. Where the focus of a study was specifically on healthcare, women’s experiences were often elicited by researchers directly; otherwise, healthcare experience was generally revealed in relation to broader questions within the research framing, such as looking at factors that influence migrant women’s management of GDM [ 69 , 70 ] or examining barriers and possible solutions to nonadherence to antidiabetic therapy [ 71 ].

Almost all studies were conducted in a primary language of the research team, with fluency in the primary language largely requisite for participation. However, there were 14 studies involving multicultural populations that allowed women to use their preferred language as research teams consisted of multilingual researchers, research assistants or interpreters (see Table 2 ).

Study findings on women with GDM experiences of healthcare

The findings from the 57 included studies were categorised into a number of salient aspects of formal healthcare experience, then further categorised as being positive and/or negative experiences depending on how participants’ self-reports were described and quoted by study authors. Where there was not an explicit reference to sentiment in the study, it has not been recorded in this review.

Mental distress

Mental distress included acute emotional reactions such as shock and stress, as well as ongoing psychological challenges in coping with GDM. The vast majority of included studies noted mental distress of some kind ( n  = 48, 84%), inferring that mental distress was inextricably part of women’s experiences of GDM and intertwined with healthcare experience.

Patient-provider interactions

From the moment diagnosis of GDM occurs, a cornerstone of women’s healthcare experience is interactions with providers, which differs depending on the model of care offered. ‘Interactions’ can be broadly defined as interpersonal encounters where communication occurs directly through conversations at consultations as well as group sessions, or interactions via other means such as text messages, emails and phone calls. Forty-four studies ( n  = 44, 77%) discussed patient-provider interactions in their findings; these were positive experiences ( n  = 9, 20%), negative experiences ( n  = 16, 36%), or ambivalent, being both positive and negative ( n  = 19, 43%). As an example of positive experience, one study reported “women were happy with the care provided in managing their GDM, acknowledging that the care was better than in their home country.” [ 62 ] In terms of negative experiences, women felt, for example, healthcare providers could be “preachy” [ 55 ] and discount their own expertise in their bodies [ 21 ]. One study [ 40 ] specifically examined the difference in women’s experiences with primary and secondary healthcare providers, and found that overall they received better care from the latter. More generally, the participants from one study emphasised the importance of a humanistic approach to care [ 76 ].

Treatment satisfaction

Treatment satisfaction was a measure reported in two quantitative studies [ 37 , 52 ], and the mixed-methods study [ 29 ]. The Diabetes Treatment Satisfaction Questionnaire (DTSQ) was used in two studies to measure satisfaction [ 29 , 37 ]. The study by Anderberg et al. [ 52 ] used its own purposely developed instrument and found 89% of women with GDM marked “satisfied”, 2% marked “neutral” and no one indicated dissatisfaction. In the study by Hussain et al. [ 37 ], which used the DTSQ, 122 (73.5%) patients reported they were satisfied with treatment and 44 (26.5%) were unsatisfied; overall, the majority of patients were satisfied with treatment but retained a ‘negative’ attitude towards GDM. The study by Trutnovsky et al. [ 29 ] went further in its analysis as women responded to the DTSQ at three different phases – before treatment, during early treatment and during late treatment – and found that overall treatment satisfaction was high, and significantly increased between early and late treatment.

Diet prescribed

Diet is a fundamental component of treatment for GDM. Once diagnosed, many women are prescribed modified diets to maintain blood sugar levels, which they record on paper or by using an electronic monitor at specified times. Thirty-nine studies ( n  = 39, 68%) included findings and discussion about women’s experiences of prescribed diet, and of those studies ( n  = 33, 84%) this is captured as generally a negative experience. In some studies, women’s experience of the prescribed diet was reported as being both positive and negative ( n  = 4, 10%); only one study ( n  = 1, 3%) recorded it as a positive experience [ 38 ]. The difficulty of following a new diet during pregnancy was a key reason as to why the experience was negative, as well as practical considerations such as being able to easily access fresh food in remote areas [ 26 ]. In studies with multicultural populations, negative experience related to managing the advice in conjunction with culturally-based diets. As noted in the two studies led by Bandyopadhyay, women had difficulty maintaining their traditional diet due to the new restrictions placed upon them [ 54 , 62 ].

Medication prescribed

Medication for GDM primarily involves some form of insulin, which is prescribed to manage blood sugar levels. Twenty-one studies ( n  = 21, 37%) included findings and discussion about women’s experiences of GDM medication and of those, it was mostly reported as being a negative experience ( n  = 13, 62%), with various reasons captured including insufficient time to “figure things out” [ 77 ] and causing feelings of anxiety and failure [ 78 ]. However, in a few studies prescribed medication was noted as being a positive experience ( n  = 3, 14%), or both a positive and negative experience ( n  = 5, 24%). In one study, a participant stated, “the fact that I’m on insulin makes it easy” [ 68 ].

Monitoring captures both the direct monitoring conducted by healthcare providers, primarily blood and blood sugar level tests as well as ultrasounds, as well as self-monitoring women were required to carry out and which was often then verified by healthcare professionals. Twenty studies ( n  = 20, 35%) included findings and discussion about women’s experiences of monitoring and of those it was seen as being negative ( n  = 14, n  = 70%), both positive and negative ( n  = 5, 25%) and positive ( n  = 1, n  = 5%). In the one study that reported positive experiences only, a participant reported that she thought it was good “they are monitoring us all the time” [ 30 ]. Studies reporting negative experiences with monitoring had participants citing reasons such as feeling over-scrutinised [ 65 ].

Access to timely healthcare

Access to healthcare can be a challenge in certain settings, and, even when access is possible, timeliness can be an issue. Of the 31 studies ( n  = 31, 54%) that referred to access in their findings, the vast majority of these studies ( n  = 28) reported access to timely healthcare being a negative experience, with reasons cited including geographic distance [ 39 , 46 ], difficulties in being able to make a booking to be seen at a hospital [ 79 ] and then, when being seen, not having enough time with a healthcare provider [ 27 , 44 ]. In one of the two studies reporting positive experiences [ 52 ], all questions relating to accessibility indicated satisfaction (97%); in the other of the two studies [ 38 ], the majority of women (68%) appreciated that health professionals took time to listen and explain.

Provision of information

Information to support women is critical in managing their GDM diagnosis. Ongoing management came from meetings with healthcare providers—described in one study as being “frontline support” [ 79 ]— alongside sources focused on diet, medication, exercise and other pertinent information. Across all the studies which discussed how provision of information by healthcare providers was received ( n  = 38, 67%), it was noted as largely negative ( n  = 24, 63%) and both positive and negative ( n  = 10, 18%), though there were discussions of positive experiences ( n  = 4, 7%). Considered together, all the studies suggested how crucial clear information is to a positive experience of healthcare. For women, having inadequate knowledge about how to cope was a source of disempowerment and, across the majority of studies ( n  = 44, 77%), participants reported they found information from providers was insufficient. Interestingly, one of these studies found the insufficiency was actually due to the information being “too much” [ 26 ], while another study [ 59 ] found there was a desire for “more frequent controls and dietary advice”. The inappropriate timing of information was also reported in a number of studies [ 31 , 58 , 79 , 80 , 81 ]. One study noted how participants found one group of healthcare providers, midwives and nurses provided better information than general practitioners [ 40 ], while another noted the contradictory nature of advice from different providers [ 82 ]. Language barriers were also identified as a problem with information provision with a lack of information available in a woman’s preferred language [ 69 ].

Financial issues

Direct healthcare costs including out-of-pocket medical consultation fees, medication and medical equipment were primarily raised by participants in the United States [ 27 ], Ghana [ 32 ] and Zimbabwe [ 45 ], with the last of these reporting that some participants discussed “the related costs of treatment … resulted in participants foregoing some of the tests and treatments ordered” [ 45 ]. A study from Canada noted a number of participants with refugee status discussed the “economic challenge” of managing GDM and that the cost of diabetes care “was quite high and difficult to manage” [ 83 ]. Several indirect costs were also discussed across the studies. In a number of studies ( n  = 7), the additional cost of purchasing healthy food to manage GDM was brought up as being a burden [ 25 , 27 , 38 , 42 , 48 , 51 , 84 ]. However, in one study, women said the costs related to food went down as being able to buy take-away (fast foods) became restricted [ 38 ]. Loss of income [ 46 ] as well as daycare costs were cited [ 25 ], as was additional transportation and hospital parking costs [ 39 , 46 , 56 ]. Finally, women in one study reported having to change occupations and even quit work to manage GDM [ 21 ].

The growing number of research studies relaying women’s GDM healthcare experience is encouraging, given increasing incidence and health burden. As this review demonstrates, there are important commonalities across all studies, suggesting that some aspects of GDM healthcare experience seem to be universal; mental distress, for example, was reported in most studies. In contrast, other aspects of GDM healthcare experience seem to relate to factors specific to local settings; financial issues were mainly raised in settings where healthcare is not universal or is not readily affordable. Related financial issues were raised by participants in a number of rural-based studies, revealing something of a difference between urban and rural healthcare settings regardless of country context.

All of the included studies relied on women’s self-reporting without necessarily involving other measures, which broadly fell into two categories: women currently undergoing care for GDM at the time of study data collection and those looking back on past experience. Included studies were overwhelmingly qualitative in design, with relatively small numbers of participants for each category; put together, though, they paint a broad picture of women’s GDM healthcare experience across a range of settings. As the phenomenon being examined here is women’s experiences, qualitative methodologies are vital given the experience of health, illness and medical intervention cannot be quantified [ 85 ]. On the other hand, quantitative studies are able to include far more participants, though it is important to note not necessarily greater applicability and generalisability; when both types of studies are considered together as in mixed-methods study designs, there is a possibility of corroboration, elaboration, complementarity and even contradiction [ 85 ].

Recruiting women through clinical and other healthcare settings, as almost all of the included studies did, necessarily leads to biased samples of participants likely to be ‘compliant’ with healthcare requirements and treatment regimens. As one study noted, compliance was high despite limited understanding of GDM and dietary requirements, as well as why change was required [ 71 ]. This scenario occurs against the backdrop of the inherent power imbalance which exists in patient-provider relationships in reproductive healthcare [ 86 ]. A few of the included studies demonstrated reflexivity for this issue, with the studies most sensitive to these concerns focused on Indigenous populations. This power imbalance also exists in patient-researcher relationships [ 87 ]; a critical way to mitigate this effect is to actively include participants in research design, which only one included study reported doing 75]. This suggests an important direction for future studies, building on recent work involving patients to establish research priorities for GDM [ 88 ]. Indeed, many of the included studies did incorporate ideas about improving healthcare as proposed by the women themselves. For example, in one study, participants reported that small group sessions with medical practitioners and more detailed leaflets would be useful [ 44 ], suggesting how current sessions could be run better.

Culturally sensitive qualitative methodologies were employed with Indigenous populations and those learnings could be further extended to other groups of research participants. GDM is known to be more common in foreign-born racial minorities [ 9 ], so it is encouraging that some studies focused on these particular groups and had study designs that included interpreters. However, this line of research is arguably under-developed given most studies excluded minoritised women who did not have a high degree of fluency in the dominant language. Language barriers were identified as a problem with information provision with GDM healthcare [ 69 , 70 ], and it is possible to extend this idea to research contexts themselves. Not being able to use the language one feels most fluent in clearly affects the way GDM healthcare experiences are reported.

Treatment satisfaction was used in both quantitative and mixed-method studies, but as a solo measure the insights it can provide is limited; we do not exactly know why or how, for example, women’s satisfaction improves later in GDM care [ 29 ]. However, a number of the studies provide possible answers. Persson et al. [ 61 ] describe the process women underwent “from stun to gradual balance” due to a process of adaptation that became easier “with increasing knowledge” about how to self-manage GDM. Ge et al. [ 89 ] found that women developed a philosophical attitude over time to reach a state of acceptance, and such a shift in attitude would clearly have an impact on how healthcare is received and understood. These findings suggest the benefit of both time and experience, and the role of these factors could be better examined with more longitudinal studies.

In this scoping review, under half of the included studies explicitly drew on theory. But as argued by Mitchell and Cody [ 90 ], regardless of whether it is acknowledged, theoretical interpretation occurs in qualitative research. Explicitly incorporating theoretical approaches are valuable in strengthening research design when such conceptual thinking clearly informs the research process; here, examining women’s lived experiences without articulating the theoretical bases which underpins research design and analysis leads to a lack of acknowledgement of relevant context as to how both treatment and research occurs. For example, gender exerts a significant influence upon help-seeking and healthcare delivery [ 91 ], and particularly for GDM. In future, it might be useful to further consider the value of theory in elucidating women’s experiences to address biases in research design to further the fields of study which relate to women’s GDM experiences [ 90 ].

Finally, much of this research has been generated in a small number of wealthy countries. GDM is a growing problem in low income settings and yet, as Nielsen et al. [ 92 ] describe, detection and treatment of GDM is hindered due to “barriers within the health system and society”. Going further, Goldenberg et al. suggest that due to competing concerns, “diagnosing and providing care to women with diabetes in pregnancy is not high on the priority lists in many LMIC”. [ 93 ] Similar barriers exist with GDM research endeavours; ensuring that evaluation of healthcare includes women’s experiences of GDM healthcare would be valuable to researchers in these settings and beyond. Thus there are clear gaps in practice as well as the research literature in considering women’s experiences of GDM healthcare internationally.

Implications

Research into women’s experience of GDM healthcare continues to accumulate and continued research efforts will contribute to far greater understanding of how we might best support women and improve healthcare outcomes. However, there is room for improvement, such as by following participants longitudinally, using mixed methods and taking more reflexive and theoretically informed approaches to researching women’s experiences of GDM healthcare. There is a need highlighted for more culturally sensitive research techniques as well as including women in the study design process, and not just as research subjects to be instrumentalised for developing recommendations for clinical delivery.

Strengths and limitations

Secondary analyses of primary research are challenging to conduct when the pool of included studies is highly heterogeneous. In this scoping review, in order to synthesise a large group of diverse studies, summarising results in terms of positive and negative experiences of GDM healthcare was reductive but necessary. This key strength of our review, inspired by sentiment analysis [ 94 ], shows the utility in capturing overall polarity of feelings as it highlights the ambivalence of healthcare experience. An additional strength was involving a research librarian to help design the searches and advise on relevant databases.

There are several limitations. For our search strategy, we used a broad set of terms relating to patient experience, but there is no standard set of terminology about this type of research, so it is possible some studies were missed. Only studies in English were included, so any studies published in other languages were missed. We did not conduct a critical appraisal on the included studies, which was a limitation; however, this was a purposeful choice in order to include a wide range of studies, including from research settings that are not as well-resourced.

This scoping review identifies commonalities in how GDM healthcare is delivered and received in settings around the world, with women’s experiences varying depending on what model of care is applied alongside other factors. Documenting experiences of GDM healthcare is a vital way to inform future policy and research directions, such as more theoretically informed longitudinal and mixed method approaches, and co-designed studies.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Dirar AM, Doupis J. Gestational diabetes from A to Z. World J Diabetes. 2017;8(12):489.

Article   Google Scholar  

Metzger BE, Contreras M, Sacks D, et al. Hyperglycemia and adverse pregnancy outcomes. New Engl J Med. 2008;358(19):1991–2002.

Article   PubMed   Google Scholar  

Kim C. Gestational diabetes: risks, management, and treatment options. Int J Women’s Health. 2010;2:339.

Article   CAS   Google Scholar  

International Diabetes Federation. IDF Diabetes Atlas. Brussels: IDF; 2019. Available from: https://www.idf.org/e-library/epidemiology-research/diabetes-atlas/159-idf-diabetes-atlas-ninth-edition-2019.html .

Xu T, Dainelli L, Yu K, et al. The short-term health and economic burden of gestational diabetes mellitus in China: a modelling study. BMJ Open. 2017;7(12):e018893.

Article   PubMed   PubMed Central   Google Scholar  

Behboudi-Gandevani S, Amiri M, Bidhendi Yarandi R, Ramezani Tehrani F. The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis. Diabetol Metab Syndr. 2019;11(1):11.

Lawrence RL, Wall CR, Bloomfield FH. Prevalence of gestational diabetes according to commonly used data sources: an observational study. BMC Pregnancy and Childbirth. 2019;19(1):349.

Australian Institute of Health and Welfare. Diabetes. Canberra: The Institute; 2020. Available from: https://www.aihw.gov.au/reports/diabetes/diabetes/contents/how-many-australians-have-diabetes/type-2-diabetes .

Pu J, Zhao B, Wang EJ, et al. Racial/ethnic differences in gestational diabetes prevalence and contribution of common risk factors. Paediatr Perinat Epidemiol. 2015;29(5):436–43.

Lavender T, Platt MJ, Tsekiri E, et al. Women’s perceptions of being pregnant and having pregestational diabetes. Midwifery. 2010;26(6):589–95.

Nielsen KK, Kapur A, Damm P, de Courten M, Bygbjerg IC. From screening to postpartum follow-up - the determinants and barriers for gestational diabetes mellitus (GDM) services, a systematic review. BMC Pregnancy and Childbirth. 2014;14:41.

Nielsen KK, Damm P, Bygbjerg IC, Kapur A. Barriers and facilitators for implementing programmes and services to address hyperglycaemia in pregnancy in low and middle income countries: a systematic review. Diabetes Res Clin Pract. 2018;145:102–18.

Costi L, Lockwood C, Munn Z, Jordan Z. Women’s experience of diabetes and diabetes management in pregnancy: a systematic review of qualitative literature. JBI Database Syst Rev Implement Rep. 2014;12(1):176–280.

Parsons J, Ismail K, Amiel S, Forbes A. Perceptions among women with gestational diabetes. Qual Health Res. 2014;24(4):575–85.

Van Ryswyk E, Middleton P, Shute E, Hague W, Crowther C. Women’s views and knowledge regarding healthcare seeking for gestational diabetes in the postpartum period: a systematic review of qualitative/survey studies. Diabetes Res Clin Pract. 2015;110(2):109–22.

He J, Chen X, Wang Y, Liu Y, Bai J. The experiences of pregnant women with gestational diabetes mellitus: a systematic review of qualitative evidence. Rev Endocr Metab Disord. 2021;22(4):777–87. Epub 2020 Nov 12.

Craig L, Sims R, Glasziou P, Thomas R. Women’s experiences of a diagnosis of gestational diabetes mellitus: a systematic review. BMC Pregnancy and Childbirth. 2020;20(1):76.

Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143.

Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.

Lawson EJ, Rajaram S. A transformed pregnancy: the psychosocial consequences of gestational diabetes. Sociol Health Illn. 1994;16(4):536–62.

Ge L, Wikby K, Rask M. Lived experience of women with gestational diabetes mellitus living in China: a qualitative interview study. BMJ Open. 2017;7(11):e017648.

Morrison MK, Lowe JM, Collins CE. Australian women’s experiences of living with gestational diabetes. Women Birth. 2014;27(1):52–7.

Gray MF, Hsu C, Kiel L, Dublin S. “It’s a very big burden on me”: women’s experiences using insulin for gestational diabetes. Matern Child Health J. 2017;21(8):1678–85.

Whitty-Rogers J, Caine V, Cameron B. Aboriginal women’s experiences with gestational diabetes mellitus: a participatory study with Mi’kmaq women in Canada. Adv Nurs Sci. 2016;39(2):181–98.

Kirkham R, King S, Graham S, Boyle JA, Whitbread C, Skinner T, et al. “No sugar”, “no junk food”, “do more exercise” - moving beyond simple messages to improve the health of Aboriginal women with Hyperglycaemia in Pregnancy in the Northern Territory - A phenomenological study. Women Birth. 2021;34(6):578–84. https://doi.org/10.1016/j.wombi.2020.10.003 . Epub 2020 Nov 2.

Article   CAS   PubMed   Google Scholar  

Collier SA, Mulholland C, Williams J, Mersereau P, Turay K, Prue C. A qualitative study of perceived barriers to management of diabetes among women with a history of diabetes during pregnancy. J Women’s Health. 2011;20(9):1333–9.

Organisation for Economic Co-operation and Development. DAC List of ODA Recipients. Paris: OECD; 2021. Available from: https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/DAC-List-ODA-Recipients-for-reporting-2021-flows.pdf .

Trutnovsky G, Panzitt T, Magnet E, Stern C, Lang U, Dorfer M. Gestational diabetes: women’s concerns, mood state, quality of life and treatment satisfaction. J Matern-Fetal Neonatal Med. 2012;25(11):2464–6.

Nicolosi BF, Lima SAM, Rodrigues MRK, et al. Prenatal care satisfaction: perception of caregivers with diabetes mellitus. Rev Bras Enferm. 2019;72(suppl 3):305–11.

Dayyani I, Maindal HT, Rowlands G, Lou S. A qualitative study about the experiences of ethnic minority pregnant women with gestational diabetes. Scand J Caring Sci. 2019;33(3):621–31.

Mensah GP, van Rooyen DRM, ten Ham-Baloyi W. Nursing management of gestational diabetes mellitus in Ghana: perspectives of nurse-midwives and women. Midwifery. 2019;71:19–26.

Kragelund Nielsen K, Vildekilde T, Kapur A, Damm P, Seshiah V, Bygbjerg IC. “If I Don’t Eat Enough, I Won't Be Healthy”. Women’s Experiences with Gestational Diabetes Mellitus Treatment in Rural and Urban South India. Int J Environ Res Public Health. 2020;17(9):3062.

Mufdlilah M, Efriani R, Rokhanawati D, Dzakiyullah NR. Mother’s obstacles in managing gestational diabetes mellitus: A Qualitative study. Ann Trop Med Public Health. 2020;23(S9):SP23942.

Khooshehchin TE, Keshavarz Z, Afrakhteh M, Shakibazadeh E, Faghihzadeh S. Perceived needs in women with gestational diabetes: a qualitative study. Electron physician. 2016;8(12):3412–20.

Kolivand M, Keramat A, Rahimi M, Motaghi Z, Shariati M, Emamian M. Self-care education needs in gestational diabetes tailored to the Iranian culture: a qualitative content analysiss. Iran J Nurs Midwifery Res. 2018;23(3):222–9.

Hussain Z, Yusoff ZM, Sulaiman SA. A study exploring the association of attitude and treatment satisfaction with glycaemic level among gestational diabetes mellitus patients. Prim Care Diabetes. 2015;9(4):275–82.

Martis R, Brown J, Crowther CA. Views and Experiences of New Zealand Women with Gestational Diabetes in Achieving Glycaemic Control Targets: The Views Study. J Diabetes Res. 2017;2017:2190812. https://doi.org/10.1155/2017/2190812 . Epub 2017 Oct 31.

Reid J, Anderson A, Cormack D, et al. The experience of gestational diabetes for indigenous Māori women living in rural New Zealand: qualitative research informing the development of decolonising interventions. BMC Pregnancy Childbirth. 2018;18:478.

Helmersen M, Sorensen M, Lukasse M, Laine HK, Garnweidner-Holme L. Women’s experience with receiving advice on diet and self-monitoring of blood glucose for gestational diabetes mellitus: a qualitative study. Scand J Prim Health Care. 2021;39(1):44–50.

Hewage S, Audimulam J, Sullivan E, Chi C, Yew TW, Yoong J. Barriers to Gestational Diabetes Management and Preferred Interventions for Women With Gestational Diabetes in Singapore: Mixed Methods Study. JMIR Form Res. 2020;4(6):e14486.

Dickson LM, Buchmann EJ, Norris SA. Women’s accounts of the gestational diabetes experience – a South African perspective. S Afr J Obstet Gynaecol. 2020;26(1):1–7.

Google Scholar  

Muhwava LS, Murphy K, Zarowsky C, Levitt N. Perspectives on the psychological and emotional burden of having gestational diabetes amongst low-income women in Cape Town, South Africa. BMC Women’s Health. 2020;20(1):231.

Hirst JE, Tran TS, My ATD, Rowena F, Morris JM, Jeffery HE. Women with gestational diabetes in Vietnam: a qualitative study to determine attitudes and health behaviours. BMC Pregnancy and Childbirth. 2012;12:10.

Mukona D, Munjanja SP, Zvinavashe M, Stray-Pederson B. Barriers of adherence and possible solutions to nonadherence to antidiabetic therapy in women with diabetes in pregnancy: Patients’ perspective. J Diabetes Res. 2017;2017:3578075.

Rasekaba T, Nightingale H, Furler J, Lim WK, Triay J, Blackberry I. Women, clinician and IT staff perspectives on telehealth for enhanced gestational diabetes mellitus management in an Australian rural/regional setting. Rural Remote Health. 2021;21(1):5983.

PubMed   Google Scholar  

Tait Neufeld H. Patient and caregiver perspectives of health provision practices for First Nations and Métis women with gestational diabetes mellitus accessing care in Winnipeg, Manitoba. BMC Health Serv Res. 2014;14:440.

Pace R, Loon O, Chan D, Porada H, Godin C, Linton J, et al. Preventing diabetes after pregnancy with gestational diabetes in a Cree community: an inductive thematic analysis. BMJ Open Diabetes Res Care. 2020;8(1):e001286.

Oster RT, Mayan MJ, Toth EL. Diabetes in pregnancy among First Nations women. Qual Health Res. 2014;24(11):1469–80.

Ge L, Wikby K, Rask M. ’Is gestational diabetes a severe illness?‘ exploring beliefs and self-care behaviour among women with gestational diabetes living in a rural area of the south east of China. Aust J Rural Health. 2016;24(6):378–84.

Abraham K, Wilk N. Living with gestational diabetes in a rural community. MCN Am J Mater Child Nurs. 2014;39(4):239–45.

Anderberg E, Berntorp K, Crang-Svalenius E. Diabetes and pregnancy: women’s opinions about the care provided during the childbearing year. Scand J Caring Sci. 2009;23(1):161–70.

McCloskey L, Sherman ML, St John M, et al. Navigating a ‘perfect storm’ on the path to prevention of type 2 diabetes mellitus after gestational diabetes: lessons from patient and provider narratives. Matern Child Health J. 2019;23(5):603–12.

Bandyopadhyay M. Gestational diabetes mellitus: a qualitative study of lived experiences of South Asian immigrant women and perspectives of their health care providers in Melbourne, Australia. BMC Pregnancy and Childbirth. 2021;21(1):500.

Nolan JA, McCrone S, Chertok IRA. The maternal experience of having diabetes in pregnancy. J Am Acad Nurs Pract. 2011;23(11):611–8.

Jarvie R. Lived experiences of women with co-existing BMI ≥ 30 and gestational diabetes mellitus. Midwifery. 2017;49:79–86.

Hjelm K, Berntorp K, Apelqvist J. Beliefs about health and illness in Swedish and African-born women with gestational diabetes living in Sweden. J Clin Nurs. 2012;21(9–10):1374–86.

Razee H, van der Ploeg HP, Blignault I, et al. Beliefs, barriers, social support, and environmental influences related to diabetes risk behaviours among women with a history of gestational diabetes. Health Promot J Aust. 2010;21(2):130–7.

Hjelm K, Bard K, Apelqvist J. Gestational diabetes: prospective interview-study of the developing beliefs about health, illness and health care in migrant women. J Clin Nurs. 2012;21(21–22):3244–56.

Hjelm K, Bard K, Apelqvist J. A qualitative study of developing beliefs about health, illness and healthcare in migrant African women with gestational diabetes living in Sweden. BMC Women’s Health. 2018;18(1):34.

Persson M, Winkvist A, Mogren I. ’From stun to gradual balance’- women’s experiences of living with gestational diabetes mellitus. Scand J Caring Sci. 2010;24(3):454–62.

Bandyopadhyay M, Small R, Davey MA, Oats JJN, Forster DA, Aylward A. Lived experience of gestational diabetes mellitus among immigrant South Asian women in Australia. Aust New Z J Obstet Gynaecol. 2011;51(4):360–4.

Carolan M, Gill GK, Steele C. Women’s experiences of factors that facilitate or inhibit gestational diabetes self-management. BMC Pregnancy and Childbirth. 2012;12:99.

Carolan M. Women’s experiences of gestational diabetes self-management: a qualitative study. Midwifery. 2013;29(6):637–45.

Parsons J, Sparrow K, Ismail K, Hunt K, Rogers H, Forbes A. Experiences of gestational diabetes and gestational diabetes care: a focus group and interview study. BMC Pregnancy Childbirth. 2018;18(1):25.

Sayakhot P, Carolan-Olah M. Sources of information on Gestational Diabetes Mellitus, satisfaction with diagnostic process and information provision. BMC Pregnancy Childbirth. 2016;16(1):287.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Evans MK, O’Brien B. Gestational diabetes: the meaning of an at-risk pregnancy. Qual Health Res. 2005;15(1):66–81.

Carolan-Olah M, Gill G, Steel C. Women’s experiences of gestational diabetes self-management: A qualitative study. Women and Birth. 2013;26(1):S2-S.

Wah YYE, McGill M, Wong J, Ross GP, Harding AJ, Krass I. Self-management of gestational diabetes among Chinese migrants: A qualitative study. Women and Birth. 2019;32(1):E17–23.

Jirojwong S, Brownhill S, Dahlen HG, Johnson M, Schmied V. Going up, going down: the experience, control and management of gestational diabetes mellitus among Southeast Asian migrant women living in urban Australia. Health Promot J Aust. 2017;28(2):123–31.

Carolan-Olah M, Duarte-Gardea M, Lechuga J, Salinas-Lopez S. The experience of gestational diabetes mellitus (GDM) among Hispanic women in a U.S. border region. Sex Reprod HealthC. 2017;12:16–23.

Hjelm K, Bard K, Nyberg P, Apelqvist J. Swedish and Middle-Eastern-born women’s beliefs about gestational diabetes. Midwifery. 2005;21(1):44–60.

Hjelm K, Bard K, Nyberg P, Apelqvist J. Management of gestational diabetes from the patient’s perspective–a comparison of Swedish and Middle-Eastern born women. J Clin Nurs. 2007;16(1):168–78.

Dayyani I, Terkildsen Maindal H, Rowlands G, Lou S. A qualitative study about the experiences of ethnic minority pregnant women with gestational diabetes. Scand J Caring Sci. 2019;33(3):621–31.

Ge L, Wikby K, Rask M. Quality of care from the perspective of women with gestational diabetes in China. Int J Gynecol Obstet. 2016;134(2):151–5.

Hui AL, Sevenhuysen G, Harvey D, Salamon E. Food choice decision-making by women with gestational diabetes. Can J Diabetes. 2014;38(1):26–31.

Draffin CR, Alderdice FA, McCance DR, et al. Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: A qualitative study. Midwifery. 2016;40:141–7.

Boyd J, McMillan B, Easton K, Delaney B, Mitchell C. Utility of the COM-B model in identifying facilitators and barriers to maintaining a healthy postnatal lifestyle following a diagnosis of gestational diabetes: a qualitative study. BMJ Open. 2020;10(8):e037318.

Hjelm K, Berntorp K, Frid A, Aberg A, Apelqvist J. Beliefs about health and illness in women managed for gestational diabetes in two organisations. Midwifery. 2008;24(2):168–82.

Hjelm K, Bard K, Nyberg P, Apelqvist J. Management of gestational diabetes from the patient’s perspective - a comparison of Swedish and Middle-Eastern born women. J Clin Nurs. 2007;16(1):168–78.

Nur Suraiya AHS, Zahara AM, Nazlena MA, Suzana S, Norazlin MI, Sameeha MJ. Perspectives of healthcare professionals and patients on management of gestational diabetes mellitus: a qualitative study in Negeri Sembilan, Malaysia. Malays J Nutr. 2016;21(3):393–9.

Siad FM, Fang XY, Santana MJ, Butalia S, Hebert MA, Rabi DM. Understanding the experiences of East African immigrant women with gestational diabetes mellitus. Can J Diabetes. 2018;42(6):632–8.

Kaptein S, Evans M, McTavish S, et al. The subjective impact of a diagnosis of gestational diabetes among ethnically diverse pregnant women: a qualitative study. Can J Diabetes. 2015;39(2):117–22.

Hammarberg K, Kirkman M, de Lacey S. Qualitative research methods: when to use them and how to judge them. Hum Reprod. 2016;31(3):498–501.

Alspaugh A, Barroso J, Reibel M, Phillips S. Women’s contraceptive perceptions, beliefs, and attitudes: an integrative review of qualitative research. J Midwifery & Women’s Health. 2020;65(1):64–84.

Råheim M, Magnussen LH, Sekse RJ, Lunde Å, Jacobsen T, Blystad A. Researcher-researched relationship in qualitative research: Shifts in positions and researcher vulnerability. Int J Qual Stud Health Well-being. 2016;11:30996.

Rees SE, Chadha R, Donovan LE, et al. Engaging patients and clinicians in establishing research priorities for gestational diabetes mellitus. Can J Diabetes. 2017;41(2):156–63.

Ge L, Albin B, Hadziabdic E, Hjelm K, Rask M. Beliefs about health and illness and health-related behavior among urban women with gestational diabetes mellitus in the south east of China. J Transcult Nurs. 2016;27(6):593–602.

Mitchell GJ, Cody WK. The role of theory in qualitative research. Nurs Sci Q. 1993;6(4):170–8.

Kuhlmann E, Annandale E. Gender and Healthcare Policy. In: Kuhlmann E, Blank RH, Bourgeault IL, Wendt C, editors. The Palgrave International Handbook of Healthcare Policy and Governance. London: Palgrave Macmillan UK; 2015. p. 578–96.

Chapter   Google Scholar  

Nielsen KK, de Courten M, Kapur A. Health system and societal barriers for gestational diabetes mellitus (GDM) services - lessons from World Diabetes Foundation supported GDM projects. BMC Int Health Hum Rights. 2012;12:33.

Goldenberg RL, McClure EM, Harrison MS, Miodovnik M. Diabetes during Pregnancy in Low- and Middle-Income Countries. Am J Perinatol. 2016;33(13):1227–35.

Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. 2012;5(1):1–167.

Download references

Acknowledgements

Jeremy Cullis, Clinical Librarian at Macquarie University, provided invaluable assistance with the database search strategy.

SP is being supported by a Macquarie Research Excellence Scholarship, funded by both Macquarie University and the Australian Government’s Research Training Program.

Author information

Authors and affiliations.

Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Rd, North Ryde, NSW, 2113, Sydney, Australia

Sheila Pham, Kate Churruca, Louise A. Ellis & Jeffrey Braithwaite

You can also search for this author in PubMed   Google Scholar

Contributions

SP led and executed the study with design support, input and advice from KC, LAE and JB. SP supported by KC assessed the literature. LAE provided statistical and methodological expertise alongside the other authors, and JB provided strategic advice. All authors reviewed and provided editorial suggestions on SP’s draft and agreed with the final submitted version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sheila Pham .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Characteristics of the studies included in the scoping review

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Pham, S., Churruca, K., Ellis, L.A. et al. A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally. BMC Pregnancy Childbirth 22 , 627 (2022). https://doi.org/10.1186/s12884-022-04931-5

Download citation

Received : 30 March 2022

Accepted : 14 July 2022

Published : 08 August 2022

DOI : https://doi.org/10.1186/s12884-022-04931-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Gestational diabetes mellitus
• Patient experience
• Delivery of healthcare
• Scoping review

BMC Pregnancy and Childbirth

ISSN: 1471-2393

• Submission enquiries: [email protected]
• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.7(1); 2020 Jan

Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review

Gwendolyn patience mensah.

1 School of Nursing and Midwifery, College of Health Sciences, University of Ghana, Ghana Legon

Wilma ten Ham‐Baloyi

2 Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth South Africa

Dalena (R.M.) van Rooyen

Sihaam jardien‐baboo.

3 Department of Nursing Science, Nelson Mandela University, Port Elizabeth South Africa

Aims and objectives

An integrative literature review searched for, selected, appraised, extracted and synthesized data from existing available guidelines on the nursing management of gestational diabetes mellitus as no such analysis has been found.

Early screening, diagnosis and management of gestational diabetes mellitus are important to prevent or reduce complications during and postpregnancy for both mother and child. A variety of guidelines exists, which assist nurses and midwives in the screening, diagnosis and management of gestational diabetes mellitus.

An integrative literature review.

The review was conducted in June 2018 following an extensive search of available guidelines according to an adaptation of the stages reported by Whittemore and Knafl (2005, Journal of Advanced Nursing , 52, 546). Thus, a five‐step process was used, namely formulation of the review question, literature search, critical appraisal of guidelines identified, data extraction and data analysis. All relevant guidelines were subsequently appraised for rigour and quality by two independent reviewers using the AGREE II tool. Content analysis was used analysing the extracted data.

Following extraction and analysis of data, two major themes were identified from eighteen ( N  = 18) guidelines. These were the need for early screening and diagnosis of gestational diabetes mellitus and for nursing management of gestational diabetes mellitus (during pregnancy, intra‐ and postpartum management). Various guidelines on the nursing management of gestational diabetes mellitus were found; however, guidelines were not always comprehensive, sometimes differed in their recommended practices and did not consider a variety of contextual barriers to the implementation of the recommendations.

Critically, scrutiny of the guidelines is required, both in terms of the best evidence used in their development and in terms of the feasibility of implementation for its context.

Relevance to clinical practice

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of gestational diabetes mellitus that provide guidance for nurse–midwives on maternal and postpartum follow‐up care for women at risk or diagnosed with gestational diabetes mellitus.

1. INTRODUCTION

The prevalence of gestational diabetes mellitus (GDM) varies per country but is estimated to be approximately 15% among pregnant women globally (Zhu & Zhang, 2016 ). However, the global prevalence is expected to increase due to increasing numbers of overweight and obese women of reproductive age (Guariguata, Linnenkamp, Beagley, Whithing, & Cho, 2014 ; Kampmann et al., 2015 ). During 2003–2014, the prevalence of pregnant women with overweight and obesity increased in high middle‐income countries mainly due to increased caloric supply and urbanization and in upper middle‐ and lower middle‐income countries as a result of the decreased employment of women in agricultural activities (Chen, Xu, & Yan, 2018 ). GDM is defined as any degree of glucose intolerance with onset or first recognition during pregnancy (American Diabetes Association [ADA], 2010 ). GDM characterizes the most common metabolic complication of pregnancy and is related to maternal complications such as hypertension, pre‐eclampsia, caesarean section, infection and polyhydramnios. It is also related to foetal morbidity in terms of macrosomia, birth trauma, hypoglycaemia, hypocalcaemia, hypomagnesemia, hyperbilirubinemia, respiratory distress syndrome and polycythemia (Mitanchez, Yzydorczyk, & Simeoni, 2015 ; Rafiq, Hussain, Jan, & Najar, 2015 ).

Additionally, women diagnosed with GDM are considerably more at risk for impaired glucose tolerance and are up to six times more likely to develop type 2 diabetes 5–10 years postpregnancy compared with women with normal glucose levels in pregnancy (Work Loss Data Institute, 2016 ). Furthermore, children from women with GDM have a higher likelihood of developing obesity and of having impaired glucose tolerance as well as diabetes, either in childhood or in early adulthood (World Health Organization [WHO], 2016 ).

Some risk factors that are identified for developing GDM include age (the risk for GDM increases with age), being overweight or obese, extreme weight gain during pregnancy and a family history of diabetes. Additional risk factors related to an increased frequency of GDM include GDM during an earlier pregnancy, a history of stillbirth or giving birth to an infant with congenital abnormalities and detection of glucose in the urine as well as ethnic background (Anna, van der Ploeg, Cheung, Huxley, & Bauman, 2008 ; Evensen, 2012 ; Kampmann et al., 2015 ; Khan, Ali, & Khan, 2013 ).

Early screening and diagnosis of GDM is therefore important to prevent or reduce complications during and postpregnancy for both mother and child. Most countries use selective screening, based on the known risk factors. Although selective screening could miss GDM cases, it could also assist nursing management by focussing health resources on women with the highest risk of complications, specifically in contexts where resources are scarce. Likewise, screening early in pregnancy for pre‐existent diabetes by determining fasting glucose is justified, especially in the context of increased existence of diabetes mellitus type 2 in young women, which often remains undiagnosed (Kampmann et al., 2015 ).

Once women are diagnosed with GDM, management includes lifestyle modifications in terms of a diet high in dietary fibre (specifically fruit and cereal) and with a low glycaemic index, as well as routine monitoring of blood glucose levels during and postpregnancy. Additionally, if needed, the GDM is treated by means of insulin, metformin and glyburide to ensure the long‐term health of the pregnant woman and her baby (ADA, 2015 ; Poomalar, 2015 ).

A guideline, developed from rigorous evidence, would assist nurses and midwives in the screening, diagnosis and management of GDM. As they are often the first point of care for women, this is particularly important in contexts where medical care is scarce. Although some guidelines on the management of GDM exist, they are often designed for medical practitioners. No study was found that summarized best practice guidelines regarding the nursing management of GDM. This study therefore searched for, selected, appraised, extracted and synthesized data from existing available guidelines to guide the development of a best practice guideline for the nursing management of GDM.

An integrative literature review was conducted following a five‐step process adopted from Whittemore and Knafl ( 2005 ). The processes proceeded as follows: Step 1: Formulation of the review question; Step 2: Literature searching; Step 3: Critical appraisal of evidence; Step 4: Data extraction; and Step 5: Data analysis. The integrative literature review was conducted by the first author, under supervision of the second and third authors, both of whom are experienced in conducting integrative literature reviews. The study was part of a larger study that aimed to develop a best practice guideline for the nursing management of GDM during the ante‐, intra‐ and postnatal periods.

2.1. Formulation of the review question

The review question (Step 1) was formulated according to the PICO format. The elements of the question were as follows: P – Population = Women; I – Issue = nursing management of GDM (including screening, diagnosis and management); C – Context = nursing and health institutions; O – Outcome = to inform best practices on the nursing management of GDM. The review question was therefore formulated as follows: What existing evidence is available to inform best practices on the nursing management of women diagnosed with GDM?

2.2. Literature searching process

The literature searching process (Step 2) was conducted with the assistance of an experienced librarian in selecting the databases and keywords. Inclusion and exclusion criteria were established to guide the search and selection process.

2.2.1. Sources of literature

Databases were thoroughly searched using the following search engines: BioMed Central, EBSCOhost (CINAHL, ERIC, Health Source: Nursing/Academic Edition, MasterFILE Premier, MEDLINE), JSTOR, PUBMED CENTRAL, SAGE, ScienceDirect, Google Scholar, Scopus and Wiley Online Library. A manual search for guidelines was performed, using Google Scholar and Google, accessing organizations specialized in developing best practice guidelines. These included Canadian Practice Guidelines, National Guidelines Clearinghouse (NGC), National Institute for Health and Clinical Excellence (NICE), Guidelines International Network, Scottish Intercollegiate Guidelines Network (SIGN), New Zealand Guidelines Group, National Health and Medical Research Council, Registered Nurses’ Association of Ontario, American College of Obstetricians and Gynaecologists, American Diabetes Association and Health Service Executives. Grey literature, such as unpublished theses and dissertations, responding to the management of GDM were also considered.

2.2.2. Key words

With the assistance of an experienced librarian, the combination of key words “guideline*” and “evidence‐based practice” and “gestational diabetes mellitus” AND “nurs* manage* OR nurs* intervention*” and “pregnan*, antenatal, intra‐natal OR postnatal*” was used. The combination of keywords used was adapted per database, if necessary, to obtain all relevant guidelines.

2.2.3. Inclusion and exclusion criteria

Guidelines were included that focussed on the nursing management of GDM where any of the following aspects are addressed: early screening for GDM and its management, self‐monitoring of blood glucose levels, lifestyle modifications and/or insulin administration. Studies published in English were used as this is the language the authors are proficient in. Guidelines published between 2004–2018 were included, and the most updated version of guidelines was included. Guidelines focussing on the management of type 1 or type 2 diabetes mellitus only were excluded as were guidelines that did not consider the practices of nurses or midwives in GDM management.

2.2.4. Search and selection process

The search for appropriate guidelines was conducted in June 2018. All guidelines that fitted the criteria for the study were retrieved and selected for inclusion. Guidelines that did not meet the required criteria were excluded. The inclusion and exclusion criteria were applied by both the first author and the fourth author (who served as an independent reviewer). Consensus regarding the inclusion and exclusion of relevant articles was reached between the authors. The search and selection process of the included guidelines is illustrated in Figure ​ Figure1’s 1 ’s PRISMA flow chart (Moher, Liberati, Tetzlaff, Altman, & PRISMA Group, 2009 ).

PRISMA flow of studies through the review (adapted from Moher et al., 2009 )

Figure ​ Figure1 1 shows that 28 guidelines were found in the literature search and retained for full‐text review. Seven guidelines were excluded, based on the study criteria, and two duplicates were excluded. Nineteen guidelines fulfilled the review criteria and were included for critical appraisal.

2.3. Critical appraisal

The AGREE II instrument was used to critically appraise the guidelines (Step 3). AGREE II consists of 23 appraisal items organized within six domains, followed by two global rating items for an overall assessment. Each domain captures a specific aspect of guideline quality. All AGREE II items were rated on a 7‐point scale (1 – “Strongly disagree”, when no relevant information was given, to 7 – “Strongly agree”, when the quality of reporting was exceptional and the criterion was fully met) (Brouwers et al., 2010 ). The rating for each item was done depending on the completeness and quality of reporting.

The overall score allocated to each guideline appraised was expressed as a percentage of the maximum possible score of 161. Guidelines with a score of 60 per cent were included as they were considered to have more rigour than guidelines with a lower score. Similarly, they were considered to contribute more weight to the discussion and recommendations derived from the review. Consensus was reached between the two reviewers (the first and fourth author), as a result of which one of the nineteen guidelines was excluded owing to poor rigour. A total of 18 guidelines were included for data extraction (Figure ​ (Figure1 1 ).

2.4. Data extraction process

After critical appraisal, data were extracted from eighteen guidelines (Step 4). This process was completed by the first and fourth authors, working independently. Data extraction focused on material relating to early screening and diagnosis of GDM and the nursing management of GDM.

2.5. Data analysis process

Thematic data analysis was used to systematically synthesize the extracted data of each guideline and develop themes (Step 5) (Burls, 2009 ). Consensus was achieved between the authors on the themes.

2.6. Ethical statement

The study obtained ethics from the University's Faculty Postgraduate Studies Committee (ethics number: H14‐HEA‐NUR‐32). The authors adhered to principles of honesty and transparency in reporting the data. Consent was not obtained, since this study had no participants.

Data extracted from the eighteen guidelines resulted in two main themes. They are, in outline, as follows: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management) (Table ​ (Table1). 1 ). Table ​ Table1 1 shows that most guidelines mentioned the nursing management of GDM during pregnancy ( N  = 17), followed by early screening and diagnosis of GDM ( N  = 16) and postpartum nursing management of GDM ( N  = 14). Intrapartum nursing management of GDM was least mentioned by the guidelines ( N  = 7). Table ​ Table2 2 provides a summary of the main recommendations per guideline, which are further discussed below.

Themes per guideline

Main recommendations per guideline

3.1. Early screening and diagnosis of GDM

Guidelines encourage early screening of the pregnant woman for possible identification and diagnosis of GDM, which can only be achieved if pregnant women are screened during antenatal visits. Scottish Intercollegiate Guidelines Network [SIGN] ( 2017 ) mentions a programme that must be designed for all pregnant women for early detection and treatment of GDM. Once women are screened and the results of the blood glucose tests fall within levels that can be diagnosed as GDM, the woman is considered as having GDM.

The timing of screening differs in the various guidelines. Most guidelines agree that early screening must be done at 24–28 weeks of gestation (American Association of Clinical Endocrinologists & American College of Endocrinology [AACE/ACE], 2010 ; Blumer et al., 2013 ; Diabetes Australia/Royal Australian College of General Practitioners [RACGP], 2016 ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; International Federation of Gynaecology & Obstetrics [FIGO], 2015 ; United States Preventative Services Taskforce [USPSTF], 2014 ) (see Table ​ Table2). 2 ). However, some guidelines recommend this to be done as early as possible or in the first trimester (Diabetes Australia/RACGP, 2016 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Society for Endocrinology, Metabolism, & Diabetes of South Africa [SEMDSA], 2017 ; FIGO, 2015 ). This often includes women that are at risk for developing GDM and, if negative, screening is repeated at 24–28 weeks of gestation (Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; FIGO, 2015 ). The International Diabetes Federation ( 2009 ) specifically recommends determination of the women's risk of developing GDM at the first antenatal visit.

The method of screening recommended also differs. Most guidelines recommend the 2‐hr 75 g oral glucose tolerance test (OGTT) to aid with the diagnosis of GDM, while some guidelines opt for other tests, including the 50 g glucose challenge (Diabetes Australia/RACGP, 2016 ; USPSTF, 2014 ), the 2‐step screening test (Permanente, 2018 ) and the HbA1c (Queensland, 2015 ; FIGO, 2015 ). However, the AACE/ACE ( 2010 ) advises against the use of the HbA1c as a screening method to diagnose GDM, while NICE ( 2015 ) does not encourage the use of other screening tests (including fasting plasma glucose, random blood glucose, HbA1c, glucose challenge tests or urinalysis for glucose) to determine the risk of a woman developing GDM. Although the 2‐hr 75 g OGTT is recommended in most guidelines, its blood glucose values to diagnose GDM differ slightly. While some (Blumer et al., 2013 ; Queensland, 2015 ; SIGN, 2017 ; SEMDSA, 2017 ; WHO, 2013 ) recommend a fasting plasma glucose of 5.1–6.9 mM, 1‐hr value of >10.0 mM and 2‐hr value 8.5–11.0, according to NICE ( 2015 ), fasting values are <5.6 mM and 2 hr 7.8mM.

Specific aspects needing consideration during early screening and diagnosis are identified by various guidelines. For example, Blumer et al. ( 2013 ) recommend that the 75g OGTT be done after at least eight (8) hours night fast but not more than fourteen (14) hours. They further recommend that the usual intake of carbohydrates by the pregnant woman should not be reduced on the days preceding the OGTT test and the pregnant woman must be seated throughout the procedure. The International Diabetes Federation ( 2009 ) recommends that women that are at high risk for developing GDM should be offered healthy lifestyle advice during their first visit when screening is done. FIGO ( 2015 ) is the only guideline that considers low‐ and high‐resource contexts in their recommendations. FIGO ( 2015 ) recommends the use of a plasma‐calibrated hand‐held glucometer with properly stored test strips to measure plasma glucose in primary care settings, particularly in low‐resource countries (where a close‐by laboratory or facilities for proper storage and transport of blood samples to a distant laboratory may not exist). Using a plasma‐calibrated hand‐held glucometer may be more convenient and reliable than test results from a laboratory done on inadequately handled and transported blood samples.

3.2. Nursing management of GDM

Nursing management of GDM is a theme that is consistently featured in the guidelines that were included in the review. GDM management includes glycaemic control and monitoring and lifestyle modifications (diet and physical activity/exercise). Recommendations included those that should be used during pregnancy and intra‐ and postpartum.

3.2.1. During pregnancy

Glycaemic control and monitoring during pregnancy must be done, for example, once a week and thereafter every 2–3 weeks until delivery (International Diabetes Federation, 2009 ), to keep blood glucose levels within acceptable ranges for pregnancy (AACE/ACE, 2010 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Permanente, 2018 ; SIGN, 2017 ; USPSTF, 2014 ; WHO, 2013 ). This is especially so where the woman is commenced on insulin therapy (AACE/ACE, 2010 ). According to Blumer et al., ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), Diabetes Australia/RACGP ( 2016 ) and ADA ( 2018 ), acceptable ranges are fasting blood sugar <5.3 mM, 1 hr pre‐prandial <7.8 mM and 2 hr postprandial <6.7 mM. Women with GDM must be encouraged to do self‐monitoring of blood glucose (ADA, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; FIGO, 2015 ). FIGO ( 2015 ) recommends that self‐monitoring should be done at least daily (low‐resource settings) and up to 3–4 times a day (high‐resource settings).

As lifestyle moderations are the first line of treatment (ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; Permanente, 2018 ; FIGO, 2015 ; USPSTF, 2014 ), pharmacological treatment should only be provided if lifestyle moderations are inadequate to keep blood glucose targets within acceptable levels after 1–2 weeks (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ; Permanente, 2018 ). The preferred pharmacological treatment is insulin (AACE/ACE, 2010 ; ADA, 2018 ; International Diabetes Federation, 2009 ; Permanente, 2018 ; SEMDSA, 2017 ; FIGO, 2015 ), while metformin and glyburide can be used as effective alternatives (AACE/ACE, 2010 ; SIGN, 2017 ; FIGO, 2015 ) if not contraindicated or unacceptable for the woman (NICE, 2015 ). However, metformin should be prescribed/continued under specialist supervision (SEMDSA, 2017 ) but is not approved in Australia (Diabetes Australia/RACGP, 2016 ).

Health education should be provided on GDM and glycaemic control, especially on recognizing the signs of hypoglycaemia and treatment of those signs. Women should be made aware of the implications of GDM for the woman and the foetus and of steps to achieve management of GDM. Family members should be taught how to use the glucometer, as well as the management principles and importance of long‐term follow‐up (Diabetes Coalition of California, 2012 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ).

In terms of diet, it is recommended that pregnant women with GDM receive nutrition counselling (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; SIGN, 2017 ; USPSTF, 2014 ), preferably from a dietician familiar with GDM (ADA, 2018 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must make it a point to involve all the necessary healthcare professionals (Queensland, 2015 ) and preferably those with expertise in GDM (International Diabetes Federation, 2009 ; SEMDSA, 2017 ). A healthy diet should be high in vegetables and protein (Permanente, 2018 ) and low in GI (International Diabetes Federation, 2009 ; NICE, 2015 ). The recommended diet should consist of a minimum intake of 1,600–1,800 kcal/day and carbohydrate intake limited to 35%–45% of total calories (Blumer et al., 2013 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Weight gain in the pregnant woman with GDM must also be checked according to her BMI (Ministry of Health Malaysia, 2017 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must encourage the pregnant woman with GDM to stick to the diet or nutrition planned with the dietician and also to monitor her blood glucose levels as scheduled.

In terms of exercise, moderate exercise is recommended, such as a 30 min’ (at least 10‐min periods) (Queensland, 2015 ) walk after meals (Blumer et al., 2013 ; NICE, 2015 ) or 1 hr a day (Permanente, 2018 ). Education should also be given about armchair exercises (American College of Obstetrics & Gynaecology [ACOG], 2018a ).

To provide the best nursing management for GDM, a customized plan of care, especially for women at high risk, should be developed (NICE, 2015 ) that is individualized and culturally sensitive (International Diabetes Federation, 2009 ). This care plan could also include checks of blood pressure and dipstick urine protein every 1–2 weeks (resourced settings) or monthly (low‐resource settings; FIGO, 2015 ; International Diabetes Federation, 2009 ; Queensland, 2015 ) as well as an ultrasound between 30–32 weeks of gestation to estimate foetal weight (Queensland, 2015 ) or every four weeks from 28–36 weeks of gestation (Ministry of Health Malaysia, 2017 ).

3.2.2. Intrapartum

Although guidelines differ regarding the delivery time and mode, most agree with an elective induction of 38–40 weeks to reduce the risk for stillbirths (Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ). A caesarean section around 40 weeks plus 6 days is recommended, but this should be done before that time for those with comorbidities or maternal or foetal complications (NICE, 2015 ; Queensland, 2015 ). The primary objective of the intrapartum nursing management of GDM is to maintain maternal euglycemia to prevent neonatal hypoglycaemia, which is caused by the hyperinsulinemia in the baby due to hyperglycaemia in the mother. Close monitoring of women with GDM during labour and delivery should therefore be done (ACOG, 2018a ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Queensland, 2015 ; SEMDSA, 2017 ) at least once an hour (ACOG, 2018a ) or, according to NICE ( 2015 ), every thirty (30) minutes till delivery. Maternal blood glucose levels must be maintained between 4.0 mM–7.0 mM (Diabetes Canada, 2018 ; Diabetes Coalition of California, 2012 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ). To achieve these blood glucose levels, the woman should be given enough glucose during labour to help her to cope with the high level of energy demands for labour and for delivery so as to prevent the woman from having hypoglycaemia (Diabetes Canada, 2018 ; NICE, 2015 ; SEMDSA, 2017 ). NICE ( 2015 ) recommends that, if the capillary plasma glucose is above 7 mM, intravenous dextrose and insulin infusion must be given during labour and delivery, although the guideline does not specify how much.

3.2.3. Postpartum

Postpartum nursing management of GDM constitutes a critical challenge when treating women with GDM. Various guidelines selected for synthesis focus on postpartum management. It is recommended blood glucose‐lowering medication should be lowered immediately after delivery (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Although guidelines recommend postpartum blood glucose screening for early detection of diabetes mellitus, impaired glucose tolerance or impaired fasting glucose (ACOG, 2018a ), they differ on when this should be done. Most guidelines recommend 6 weeks when the woman comes for postnatal follow‐up (Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ) or between 6–12/13 weeks (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Queensland, 2015 ). Blumer et al. ( 2013 ) is the only guideline that recommends, besides the 6‐ to 12‐week screening, that blood glucose monitoring should also be done 24–72 hr after delivery. This is to rule out high blood glucose levels just after delivery.

Most guidelines prefer a follow‐up of screening varying between 1 year (NICE, 2015 ; Permanente, 2018 ; SEMDSA, 2017 ) and 3 years (ACOG, 2018a ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ). According to ADA ( 2018 ), risk factors should be considered when deciding the timeframe for follow‐up screening. According to Diabetes Canada ( 2018 ), emails and phone calls can be used to remind women for their follow‐up screening. The method of screening recommended also differs, although a 2‐hr 75 g OCTT seems to be the most frequently used, as recommended by nine ( N  = 9) guidelines. ACOG ( 2018a ) recommend that women with impaired glucose tolerance or with impaired fasting glucose must be referred as early as practicable for prevention therapy.

In addition, women with a history of GDM must be counselled on preventative lifestyle modifications to reduce the risk of type 2 diabetes (ACOG, 2018a ; ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; SIGN, 2017 ; FIGO, 2015 ) specifically regarding their diet, weight control and exercise requirements (SIGN, 2017 ). Referral to a dietician can be done (Diabetes Canada, 2018 ). According to NICE ( 2015 ) women should be educated specifically with regard to the signs and symptoms of hyperglycaemia. Education on the risk of developing GDM in subsequent pregnancies should be included as well as the benefits of optimizing postpartum and inter‐pregnancy weight (Queensland, 2015 ).

Various guidelines (American College of Obstetrics and Gynaecology [ACOG], 2018b ; International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ) recommend that women with GDM should be encouraged to breastfeed their newborns immediately after delivery, thereby helping to prevent hypoglycaemia in the newborn. It is recommended that continuous breastfeeding should be done for at least 3–4 months postpartum (Diabetes Canada, 2018 ; SIGN, 2017 ) or longer (Ministry of Health Malaysia, 2017 ) as this helps to reduce childhood obesity, glucose intolerance and diabetes later in life. However, caution should be advised regarding maternal hypoglycaemia if breastfeeding (SEMDSA, 2017 ) and skilled lactation support is therefore recommended (Queensland, 2015 ; FIGO, 2015 ). Finally, extra attention is also required to detect early signs of genitourinary, uterine and surgical site infections (in the case of an episiotomy and caesarean delivery; FIGO, 2015 ).

4. DISCUSSION

4.1. comprehensiveness of the guidelines.

Several guidelines from a variety of healthcare organizations, associations or health departments were found that include aspects relevant to the nursing management of GDM. Not all guidelines focus on all aspects (namely glycaemic control, monitoring and treatment and lifestyle moderations, including diet and physical activity/exercise) and phases of the nursing management of GDM (during pregnancy, intrapartum as well as postpartum) as only 8 ( N  = 8) of the guidelines reviewed include all phases of the management of GDM (ACOG, 2018a ; Diabetes Canada, 2018 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; SEMDSA, 2017 ; FIGO, 2015 ). There were guidelines which cover some of the phases or the nursing management of GDM in general. For example, the SIGN ( 2017 ) guideline does not focus on the nursing management of GDM during labour and delivery but does provide general recommendations on what should be done during pregnancy and postdelivery. NGC ( 2013 ) also does not discuss intrapartum nursing management of GDM but gives recommendations on the testing and diagnosis of pregnant women.

Guidelines also differed in the level of descriptiveness employed. Guidelines that were generally more descriptive with their recommendations included those from Blumer et al. ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), NICE ( 2015 ), SEMDSA ( 2017 ) and Diabetes Canada ( 2018 ). Additionally, variances in best practices regarding screening and diagnosis as well as the nursing management of GDM were observed. It is thus recommended that existing guidelines should be scrutinized in respect of their level of descriptiveness, together with the latest best evidence and of the quality of the evidence used to develop the recommendations in the guidelines.

4.2. Quality of evidence

Not all guidelines reviewed included the level or grades of evidence used for each recommendation and various levels or grades were used. This is required to select a recommendation for implementation that fits the context best and will yield the best outcomes for both mother and child. For example, some of the guidelines included did not use a grading system for evidence or references when citing the recommendations (Diabetes Coalition of California, 2012 ; NGC, 2013 ; NICE, 2015 ; Permanente, 2018 ), while others did not use a grading system for the evidence included, but did use a variety of evidence when citing the recommendations (International Diabetes Federation, 2009 ; Queensland, 2015 ; SEMDSA, 2017 ; USPSTF, 2014 ). Other guidelines included grading systems for the evidence of which an A–D grading system was the most commonly used which was adapted from the American Diabetes Association ( 2018 ). Grade A refers to clear evidence from well‐conducted, generalizable RCTs, grade B includes supportive evidence from well‐conducted cohort studies, while grade C and grade D refers to supportive evidence from poorly controlled or uncontrolled studies as well as expert consensus or clinical experience, respectively. Some guidelines included a variety of evidence supporting the recommendations (grade A–D) (AACE/ACE, 2010 ; Diabetes Australia/RACG, 2016 ; WHO, 2013 ), with two guidelines mainly using grade A and B evidence (ADA, 2018 ; Blumer et al., 2013 ), another two guidelines mainly using grade B and C evidence (ACOG, 2018a ; SIGN, 2017 ) and a fifth guideline mainly using grade C and D evidence to support the recommendations (Diabetes Canada, 2018 ). FIGO ( 2015 ) used the 2019 grading system, including mostly moderate quality evidence (+++) and very low‐quality evidence (+), while the guideline by the Ministry of Health Malesia ( 2017 ) used a grading system from the United States/Canadian Preventive Services Task Force ( 2001 ) where level I (at least one properly conducted RCT) and level III (expert opinions) were mostly used to support the recommendations. Therefore, in this review it was impossible to make a valid statement for each recommendation that was based on evidence grades/levels. A systematic review is therefore recommended which extends beyond the AGREEII tool that was undertaken in this study to summarize the overall strength of evidence of each recommendation, such as the screening, diagnosis and nursing management of GDM during pregnancy, intrapartum and postpartum care and the overall quality of each particular guideline. Additionally, only two guidelines considered the input from the woman in the management of GDM (International Diabetes Federation, 2009 ; NICE, 2015 ). Any recommendation or care plan developed should be discussed with the woman diagnosed with GDM and her permission should be obtained to implement the recommended care practices.

4.3. Resources/Barriers

Only one guideline considered the context in terms of low/high resources (FIGO, 2015 ). The reality is that most low‐resource countries are unable to implement some of the recommendations, such as, for example, universal 75‐g OGTT or self‐monitoring every day (FIGO, 2015 ). The possible barriers to the implementation of the recommendations caused by a lack of resources were not addressed in most of the guidelines. For example, several barriers to maternal health related to GDM have been identified. These include the lack of trained healthcare professionals; high staff turnover; lack of standard protocols and diagnostic tools, consumables and equipment; inadequate levels of financing of health services and treatment; and lack of or poor referral systems, feedback mechanisms and follow‐up systems.

Further barriers relate to distance to health facility; perceptions of female body size and weight gain/loss related to pregnancy; practices related to a pregnant women's diet; societal negligence of women's health; lack of decision‐making power among women regarding their own health; the role of women in society and expectations that the pregnant woman move to her maternal home for delivery; and lack of adherence to recommended postpartum screening and low continued lifestyle modifications ( 2017 , & Stray‐Pederson, 2 2017 ; Nielsen, Courten, & Kapur, 2012 ; Nielsen, Kapur, Damm, Courten, & Bygbjerg, 2014 ). Additionally, a recent delivery experience, baby's health issues, personal and family adjustment to the new baby, a negative experience of medical care and services and concerns about postpartum and future health (as in, for example, fear of being informed that they have diabetes) were specifically related to the barriers to postpartum follow‐up care (Bennett et al., 2011 ).

The barriers cited should be considered when implementing the recommendations offered by the guidelines. Further, an integration of health services should be offered as well as communication between the different healthcare professionals is required. Integration of health services can be done when postpartum follow‐up of a mother can be combined with the child's vaccination and routine paediatric care.

4.4. Recommendations

Kaiser and Razurel ( 2013 ) examined the determinants of health behaviours during the postpartum period in GDM patients. They found that the women's physical activity and diet do not often meet the recommended health‐promoting actions. Risk perception, health beliefs, social support and self‐efficacy were the main factors that were identified as having an impact on the adoption of health behaviours. GDM clients are encouraged to engage in lifestyle modifications or healthy behaviours during the postpartum period. It is important, therefore, to identify the factors that may influence these clients to continue with healthy behaviours (Kaiser & Razurel, 2013 ).

Education of the woman diagnosed with GDM on the screening, and management (including preventative lifestyles) is imperative and will assist in addressing some of the above‐mentioned barriers. Education, as mentioned by most guidelines, should preferably be given by nurses and/or midwives to all pregnant women that are at risk or diagnosed with GDM. Furthermore, the healthcare professionals will need to be trained on pregnancy‐specific lifestyle modifications, treatment and screening for complications (International Diabetes Federation, 2009 ). Finally, it is particularly important for low‐resource settings that availability of trained healthcare professions, self‐monitoring equipment and insulin supply, and laboratory resources for clinical monitoring of glucose control and assessment of renal damage (International Diabetes Federation, 2009 ) should be prioritized in national budgets for health care.

No contextualized guideline on the nursing management of GDM is available for contexts where women with GDM deal with specific challenges such as factors related to the health system, or socioeconomic and cultural conditions that may impose barriers to the implementation of the best practice. It is therefore recommended that, prior to the implementation, a context analysis should be conducted to identify specific barriers to its implementation. This was confirmed by FIGO ( 2015 ) who mentioned that local decisions will be required to decide whether a selective or universal approach will be used for each individual patient. Additionally, further research of the barriers is required to develop contextualized guidelines considering the challenges some women and some health systems may have in accessing or providing adequate maternal health care. The developed contextualized guidelines could then be piloted. Piloting will be done to determine how the guidelines could have a positive effect on the nursing management of GDM while considering the input from the pregnant women as well as possible barriers or resource constraints towards its implementation.

4.5. Limitations

Some limitations of the study were observed. A comprehensive search of a variety of databases available to the authors was used with the assistance of an experienced librarian. However, limited databases were available, and some organizations/ developers of guidelines were not subscribed to so some guidelines may have been missed. Although the reviewer possessed wide experience in appraising the guidelines, more independent reviewers could have reduced possible bias in the selection process of the guidelines.

5. CONCLUSION

Data extracted from the eighteen guidelines resulted in two main themes: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management). Although a variety of guidelines on the management of GDM were found, guidelines were not always comprehensive, sometimes differed in recommended practices and did not consider barriers to the implementation of the recommendations.

6. RELEVANCE TO CLINICAL PRACTICE

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of GDM. The findings can be used by nurse–midwives when conducting maternal and postpartum follow‐up care for women at risk or diagnosed with GDM. However, critically scrutinizing the guidelines in terms of the best evidence used in their development and feasibility of the implementation of the recommendations for its context is required. Additionally, education of women with GDM could assist in addressing any barriers such as certain harmful health beliefs, a lack of social support and self‐efficacy to provide the best maternal health care. Further research is recommended to determine the strength of evidence of each recommendation and the development and implementation of a contextual guideline on the management of GDM that considers possible barriers and resource constraints towards its implementation.

CONFLICT OF INTEREST

The authors have no conflicts of interest to disclose.

ACKNOWLEDGEMENTS

The authors would like to thank Vicki Igglesden for editing the manuscript.

Mensah GP, ten Ham‐Baloyi W, van Rooyen DRM, Jardien‐Baboo S. Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review . Nursing Open . 2020; 7 :78–90. 10.1002/nop2.324 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• American Association of Clinical Endocrinologists and American College of Endocrinology [AACE/ACE] (2015). Clinical practice guidelines for developing a diabetes mellitus comprehensive care plan . American Association of Clinical Endocrinologists; Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959114/pdf/nihms-803042.pdf [ PMC free article ] [ PubMed ] [ Google Scholar ]
• American Diabetes Association [ADA] (2010). Diagnosis and classification of diabetes mellitus . Diabetes Care , 33 ( 1 ), 6269. [ Google Scholar ]
• American College of Obstetrics and Gynaecology [ACOG] (2018a). Gestational diabetes mellitus. ACOG Gestational diabetes resource overview . Retrieved from https://www.acog.org/Womens-Health/Gestational-Diabetes [ Google Scholar ]
• American College of Obstetrics and Gynaecology [ACOG] (2018b). ACOG practice bulletin no. 190: Gestational diabetes mellitus . Obstetrics Gynecology , 131 ( 2 ), e49–e64. https://orcid.org/10.1097/AOG.0000000000002501 [ PubMed ] [ Google Scholar ]
• American Diabetes Association (2018). Strategies for improving care. Sec. 1. In: Standards of medical care in diabetes . Diabetes Care , 36 ( Supplement 1 ):S6–S12. [ Google Scholar ]
• Anna, V. , van der Ploeg, H. P. , Cheung, N. W. , Huxley, R. R. , & Bauman, A. E. (2008). Socio‐demographic correlates of the increasing trend in prevalence of gestational diabetes mellitus in a large population of women between 1995 and 2005 . Diabetes Care , 31 ( 12 ), 2288–2293. 10.2337/dc08-1038 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Association and [ADA] (2015). Management of diabetes in pregnancy . Diabetes Care , 38 ( Supplement 1 ), S77–S79. 10.2337/dc15-S015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Association and [ADA] (2018). Management of diabetes in pregnancy: Standards of medical care in diabetes . Diabetes Care , 41 ( Suppl. 1 ), S137–S143. [ PubMed ] [ Google Scholar ]
• Bennett, W. L. , Ennen, C. S. , Carrese, J. A. , Hill‐Briggs, F. , Levine, D. M. , Nicholson, W. K. , & Clark, J. M. (2011). Barriers to and facilitators of postpartum follow‐up care in women with recent gestational diabetes mellitus: A qualitative study . Journal of Women’s Health (Larchment) , 20 ( 2 ), 239–245. 10.1089/jwh.2010.2233 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blumer, I. , Hadar, E. , Hadden, D. R. , Jovanovič, L. , Mestman, J. H. , Murad, M. H. , & Yogev, Y. (2013). Diabetes and pregnancy: An endocrine society clinical practice guideline . Journal of Clinical Endocrinology & Metabolism , 98 ( 11 ), 4227–4249. 10.1210/jc.2013-2465 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brouwers, M. , Kho, M. E. , Browman, G. P. , Burgers, J. S. , Cluzeau, F. , Feder, G. , & AGREE Next Steps Consortium (2010). AGREE II: Advancing guideline development, reporting and evaluation in healthcare . Canadian Medical Association Journal , 182 , E839–842. 10.1503/cmaj.090449 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burls, A. (2009). What is critical appraisal? Evidence based medicine . Retrieved from http://www.bandolier.org.uk/painres/download/whatis/What_is_critical_appraisal.pdf [ Google Scholar ]
• Chen, C. , Xu, X. , & Yan, Y. (2018). Estimated global overweight and obesity burden in pregnant women based on panel data model . PLoS ONE , 13 ( 8 ), e0202183 10.1371/journal.pone.0202183 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Diabetes Australia/Royal Australian College of General Practitioners [RACGP] (2016). General practice management of type 2 diabetes . East Melbourne: RACGP. [ Google Scholar ]
• Diabetes Canada (2018). Clinical practice guidelines . Retrieved from http://guidelines.diabetes.ca/docs/CPG-2018-full-EN.pdf [ Google Scholar ]
• Diabetes Coalition of California (2012). Basic guidelines for diabetes care . Retrieved from https://www.diabetescoalitionofcalifornia.org/guidelines/ [ Google Scholar ]
• Evensen, A. E. (2012). Update on gestational diabetes mellitus . Primary Care , 9 ( 1 ), 8394 10.1016/j.pop.2011.11.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Working Group (2019). Criteria for applying or using GRADE . Retrieved from http://www.gradeworkinggroup.org/index.htm [ Google Scholar ]
• Guariguata, L. , Linnenkamp, U. , Beagley, J. , Whiting, D. R. , & Cho, N. H. (2014). Global estimates of the prevalence of hyperglycaemia in pregnancy . Diabetes Research in Clinical Practice , 103 ( 2 ), 176–185. 10.1016/j.diabres.2013.11.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• International Diabetes Federation (2009). Pregnancy and diabetes booklet (p. 136). Brussels: International Diabetes Federation. [ Google Scholar ]
• Kaiser, B. , & Razurel, C. (2013). Determinants of postpartum physical activity, dietary habits and weight loss after gestational diabetes mellitus . Journal of Nursing Management , 21 ( 1 ), 5869. [ PubMed ] [ Google Scholar ]
• Kampmann, U. , Madsen, L. R. , Skajaa, G. O. , Iversen, D. S. , Moeller, N. , & Ovesen, P. (2015). Gestational diabetes: A clinical update . World Journal of Diabetes , 6 ( 8 ), 1065–1072. 10.4239/wjd.v6.i8.1065 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Khan, R. , Ali, K. , & Khan, Z. (2013). Socio‐demographic risk factors of gestational diabetes mellitus . Pakistan Journal of Medical Sciences , 29 ( 3 ), 843846 10.12669/pjms.293.3629 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ministry of Health Malaysia (2017). Clinical practice guidelines: Management of diabetes in pregnancy . Putrajaya, Malaysia: Malaysia Health Technology Assessment Section. [ Google Scholar ]
• Mitanchez, D. , Yzydorczyk, C. , & Simeoni, U. (2015). What neonatal complications should the paediatrician be aware of in case of maternal gestational diabetes? World Journal of Diabetes , 6 ( 5 ), 734–743. 10.4239/wjd.v6.i5.734 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moher, D. , Liberati, A. , Tetzlaff, J. , & Altman, D. G. , & PRISMA Group (2009). Preferred reporting items for systematic reviews and meta‐analyses: The PRISMA statement . PLoS Medicine , 6 ( 7 ), e1000097 10.1371/journal.pmed.1000097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mukona, D. , Munjanja, S. P. , Zvinavashe, M. , & Stray‐Pederson, B. (2017). Barriers of adherence and possible solutions to nonadherence to antidiabetic therapy in women with diabetes in pregnancy: Patients' perspective . Journal of Diabetes Research , 2017 , 1–10. 10.1155/2017/3578075 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• National Guideline Clearinghouse [NGC] (2013). Work Loss Data Institute. Diabetes (Type 1, 2 and gestational) . Retrieved from https://www.ahrq.gov/gam/index.html [ Google Scholar ]
• National Institute for Healthcare and Excellence [NICE] (2015). Diabetes in pregnancy: management from preconception to the postnatal period . Retrieved from https://www.nice.org.uk/guidance/ng3/resources/diabetes-in-pregnancy-management-from-preconception-to-the-postnatal-period-51038446021 [ PubMed ] [ Google Scholar ]
• Nielsen, K. K. , de Courten, M. , & Kapur, A. (2012). Health system and societal barriers for gestational diabetes mellitus (GDM) services ‐ lessons from World Diabetes Foundation supported GDM projects . BMC International Health Human Rights , 12 , 33 10.1186/1472-698X-12-332012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nielsen, K. K. , Kapur, A. , Damm, P. , de Courten, M. , & Bygbjerg, I. C. (2014). From screening to postpartum follow‐up – the determinants and barriers for gestational diabetes mellitus (GDM) services: A systematic review . BMC Pregnancy Childbirth , 14 , 41 10.1186/1471-2393-14-41 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Permanente, K. (2018). Gestational diabetes screening and treatment guideline (p. 112). Washington: Kaiser Foundation Health Plan of Washington. [ Google Scholar ]
• Poomalar, G. K. (2015). Changing trends in management of gestational diabetes mellitus . World Journal of Diabetes , 6 ( 2 ), 284–295. 10.4239/wjd.v6.i2.284 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Queensland (2015). Gestational diabetes mellitus . Queensland: Queensland Government. [ Google Scholar ]
• Rafiq, W. , Hussain, S. Q. , Jan, M. , & Najar, B. A. (2015). Clinical and metabolic profile of neonates of diabetic mothers . International Journal of Contemporary Pediatricians , 2 ( 2 ), 114118 10.5455/2349-3291.ijcp20150510 [ CrossRef ] [ Google Scholar ]
• Scottish Intercollegiate Guidelines Network [SIGN] . (2017). Management of diabetes . Retrieved from http://www.sign.ac.uk/assets/qrg116.pdf [ Google Scholar ]
• Society for Endocrinology, Metabolism and Diabetes of South Africa [SEMDSA] . (2017). SEMDSA 2017 Guidelines for the management of type 2 diabetes mellitus . Journal of Endocrinology, Metabolism and Diabetes of South Africa , 22 ( 1 Supplement 1 ), S1S196. [ Google Scholar ]
• The International Federation of Gynecology and Obstetrics [FIGO] (2015). The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management and care . International Journal of Gynaecology and Obstetrician , 131 ( S3 ), S173–S211. [ PubMed ] [ Google Scholar ]
• United States Preventive Services Task Force [USPSTF] (2014). Recommendation statement USPSTT screening for gestational diabetes mellitus: U.S. Preventive Services Task Force Recommendation Statement . Annals International Medicine , 160 ( 6 ), 414422. [ PubMed ] [ Google Scholar ]
• United States/Canadian Preventive Services Task Force (2001). Screening for otitis media with effusion: Recommendation statement from the Canadian Task Force on Preventive Health Care . Canadian Medical Association Journal , 165 ( 8 ), 1092–1093. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Whitemore, R. , & Knafl, K. (2005). The integrative review: Updated methodology . Journal of Advanced Nursing , 52 ( 5 ), 546–553. 10.1111/j.1365-2648.2005.03621.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Work Loss Data Institute (2016). Guideline summaries . Retrieved from https://www.guidelinecentral.com/shop/gestational-diabetes-pocket-guide/ [ Google Scholar ]
• World Health Organization [WHO] (2013). Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy (p. 2013). Geneva, Switzerland: WHO. [ PubMed ] [ Google Scholar ]
• World Health Organization [WHO] (2016). Global report on diabetes . World Health Organisation; Retrieved from http://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionxml:id=87B0BCA0CBD924FCD2E6C34E791C2FDF?sequence=1) [ Google Scholar ]
• Zhu, Y. , & Zhang, C. (2016). Prevalence of gestational diabetes and risk of progression to type 2 diabetes: A global perspective . Current Diabetes Report , 16 , 7 10.1007/s11892-015-0699-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Open access
• Published: 31 August 2024

The association between the Helicobacter pylori infection and the occurrence of gestational diabetes: a systematic review and meta-analysis

• Parisa Kohnepoushi 1 ,
• Rozhin Mansouri 1 ,
• Ali Baradaran Moghaddam 2 ,
• Marzieh Soheili 3 ,
• Hamed Gilzad Kohan 3 &
• Yousef Moradi 4  

Journal of Health, Population and Nutrition volume  43 , Article number:  136 ( 2024 ) Cite this article

Metrics details

This meta-analysis aims to establish a more precise association between gestational diabetes mellitus (GDM) incidence and H. pylori infection by amalgamating findings from prior case–control and cohort studies.

To identify relevant studies, we conducted a comprehensive search using the Excerpta Medica Database (Embase), PubMed (Medline), Web of Science (ISI), and Scopus from January 1990 to November 2022. The screening process involved reviewing the entire text, abstracts, and titles of retrieved articles. Subsequently, data extraction was performed from the selected articles, and their quality was assessed using the Newcastle–Ottawa Scale checklist. Version 17 of STATA software was utilized for the analysis, with relative risks (RR) calculated along with their 95% confidence intervals (CI) to quantify the impact of the included studies.

This meta-analysis included eight observational and analytical studies. The combined risk of gestational diabetes mellitus (GDM) in pregnant women with H. pylori infection was found to be 1.97 times higher compared to pregnant women without infection (RR: 1.97; 95% CI 1.57–2.47; I 2  = 0.00%; P  = 0.84).

Pregnant women with H. pylori infection are at an increased risk of developing gestational diabetes.

Introduction

Diabetes is known as the third silent killer in the world. It is characterized by elevated blood glucose levels caused by deficiencies in insulin secretion or abnormalities in cellular function. To make informed clinical and public health decisions, it is crucial to carry out research in this area to identify the risk factors for illness [ 1 , 2 ]. Previous studies conducted in the United States have revealed that 7% of pregnant women have diabetes, with 86% of cases being gestational diabetes mellitus (GDM), which develops during pregnancy [ 3 , 4 ]. Globally, the prevalence of GDM ranges from 5 to 25.5%, depending on several factors including age, race, ethnicity, and body composition, in addition to the screening and diagnostic standards that have been used [ 1 ].

GDM is a high-risk pregnancy-related illness that endangers the health of both the mother and the fetus. It is also considered a primary cause of premature birth, abortion, miscarriage, and infant mortality [ 1 ]. Due to the physiological and mechanical changes that occur during pregnancy, mothers are vulnerable to a variety of opportunistic diseases, particularly common viral and bacterial infections [ 5 , 6 , 7 ]. Helicobacter pylori ( H. pylori ) is the sole kind of microorganism known to survive in the human stomach. It damages the gastric mucosa and is thought to be the primary cause of chronic stomach disorders. Serious digestive system issues, including stomach ulcers and stomach cancer, are more likely to occur when H. pylori infection is present [ 8 , 9 ].

H. pylori infections affect about 50% of people worldwide, with infection rates being greater in developing countries. Studies on public health have linked H. pylori to several extra-gastrointestinal conditions, including neurological disorders, cardiovascular conditions, and hematologic conditions (such as idiopathic thrombocytopenic purpura and unexplained iron deficiency anemia). More recently, studies on midwifery have raised the possibility that H. pylori infection may affect expecting mothers [ 10 , 11 ]. Pregnant women have been found to have a significantly higher level of H. pylori IgM test positivity than non-pregnant women in published research [ 12 , 13 , 14 ]. Based on the results of the H. pylori IgM test, which detects both recent and past infections, it is plausible to assume that many infections occur during pregnancy. Given that pregnancy induces immune adaptations to foster tolerance towards the semi-allogeneic fetus, these physiological changes could make pregnant mothers more susceptible to H. pylori infection [ 15 ].

While pregnancy often maintains innate and humoral immunity, it tends to decrease cellular cytotoxic immune responses. Given that H. pylori infection is most likely contracted before pregnancy, it is widely recognized that hormonal and immune system changes during pregnancy may contribute to the activation of a latent H. pylori infection [ 15 , 16 ]. Although the mentioned studies suggest a potential link between H. pylori and the development of GDM, it remains unproven whether the combined risk of high blood sugar and H. pylori infection increases the likelihood of pregnancy-related illnesses and impedes fetal development [ 3 ]. During pregnancy, virulent strains of H. pylori , particularly the cag + strain, can increase insulin resistance. They do this by inducing inflammatory factors such as 71, IL6, and CRP. Moreover, chronic inflammation triggered by H. pylori impacts the hormones that regulate insulin production in the stomach and duodenum. Additionally, this inflammation affects the pancreatic B cells responsible for insulin production, leading to a decrease in insulin secretion. The cag + strain specifically influences the gastric somatostatin hormone, resulting in reduced insulin release from the pancreas [ 16 , 17 ]. Furthermore, H. pylori increases and decreases the levels of the ghrelin and leptin hormones, respectively, which predisposes pregnant women to diabetes [ 17 ].

This meta-analysis was conducted in response to the previously mentioned discrepancies in studies exploring the relationship between H. pylori infection and the incidence of GDM. It aimed to examine this association more accurately by synthesizing the results from published case–control and cohort studies. The systematic review and meta-analysis methodology used in this study contributes to the overall research question by providing a complete and robust synthesis of the available information on the relationship between H. pylori infection and the incidence of GDM. By combining data from different analytical observational studies, this method improves the statistical power, precision and generalizability of the results and provides valuable insights into the subject of the study.

This study is a systematic review and meta-analysis conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, specifically following the PRISMA 2020 guidelines. It involved six fundamental steps: search syntax and strategy, screening, selection, data extraction, quality assessment, and meta-analysis [ 18 ]. The protocol of this meta-analysis is registered in Prospero (CRD42023422182).

Search strategy and keywords

In this meta-analysis, we utilized primary search terms and their synonyms identified through Mesh, Thesauruses, and EMTREE. The databases searched included PubMed (Medline), Excerpta Medica Database (Embase), Scopus, and Web of Science (ISI), covering publications from January 1, 1990, to November 30, 2022. We combined keywords related to GDM and H. pylori infection for the database search (Table  1 ). The authors conducted a grey literature search using Google Scholar and hand searching methods to locate relevant resources. All results were then compiled using Endnote software version 8. The screening process, based on titles, abstracts, and full texts, eliminated repetitive studies, considering their titles, authors, and publication years. Publications unrelated to the study’s focus were excluded. Additionally, a manual search was conducted to include relevant studies and their references. Two independent reviewers, PK and RM, handled the screening phase.

Inclusion and exclusion criteria

The PECOT framework was utilized to define the inclusion criteria for this study. All case–control and cohort studies that identified a relationship between H. pylori infection and GDM in pregnant women were eligible for inclusion. The criteria, structured on the PECOT framework, are outlined in Table  1 . Also the exclusion criteria listed in Table  1 . In cases where the full text of a study that met the inclusion criteria was not available, we initially contacted the authors via email. If there was no response, those studies were excluded from the analysis. The selection and screening of articles for this meta-analysis were independently conducted by two authors, RM and PK.

Data extraction

Following the screening phases according to the inclusion and exclusion criteria, an information extraction checklist was created to extract data from the final articles based on the checklist. This information included details about the studies (such as the authors’ names, publication years, types of studies, countries, and sample sizes), the intended population (such as the age of pregnant mothers, gestational age, and type of population investigated in the studies), the outcome (such as the desired effect size in the studies along with the 95% confidence interval (CI)), and information related to the desired exposure (such as the diagnosis method of H. pylori infection).

Quality assessment

Two of the authors (PK/MS and YM) conducted a qualitative evaluation of studies based on the Newcastle–Ottawa Quality Assessment Scale (NOS) checklist. This checklist is designed to evaluate the quality of observational studies, especially case–control and cohort studies.

Statistical analysis

To calculate the pooled relative risk (RR) with a 95% CI, authors utilized the Meta set package, which accounted for the logarithm and standard deviation of the RR logarithm. The choice of RR as the general effect size was based on the low prevalence of GDM in the exposed groups (pregnant women with H. pylori ), which is less than 5% [ 19 ]. In this meta-analysis, we combined odds ratio (OR) values from case–control studies with risk ratio (RR) values from cohort studies, reporting them collectively as RR effect sizes. Study heterogeneity was assessed using I 2 and Cochrane’s Q test, where 0–25% indicates minimal heterogeneity, and 75–100% indicates high heterogeneity. Also, authors utilized the random-effects model (REM) for calculating overall pooled estimates and the fixed-effects model (FEM) for reporting and conducting subgroup analyses. Publication bias was evaluated using Egger’s test and the funnel plot. All statistical analyses were conducted using STATA 17.0, with a P -value of less than 0.05 considered significant. Subgroup analyses were based on the age of pregnant mothers, gestational age, and different H. pylori diagnosis methods. Furthermore, Meta-regression analysis was also performed to establish the linear association between various parameters such as maternal age and gestational age and the strength of the correlation between H. pylori infection and GDM.

In this meta-analysis, 8 observational and analytical studies were evaluated, as shown in Fig.  1 and Table 2 . After searching and retrieving papers from international databases, 322 articles from PubMed, 480 from Scopus, and 109 from Web of Science were discovered for this meta-analysis. Initially, 499 items were excluded for duplication. Subsequently, 412 articles were subjected to title screening, and 289 articles were deleted owing to irrelevant titles. In the following phase, 123 articles were evaluated based on their abstracts, with 25 articles remaining for additional study. Following full-text screening, 17 articles were removed because they were irrelevant to the outcome and effect size or the methodology used. Finally, the meta-analysis included eight publications from case–control and cohort studies. The 17 publications were excluded because of their lack of relevance to the outcome and effect size, as well as the methodology used (Fig.  1 ).

A flow diagram demonstrating the study selection process

The sample sizes of these studies ranged from 20 to 2820 pregnant women. The highest and lowest effect sizes, related to the presence of H. pylori infection and the occurrence of GDM, were observed in the studies by Shimos et al. [ 20 ] and Fu et al. [ 10 ], respectively. When these studies’ results were combined, the pooled RR of GDM in pregnant women with H. pylori infection was found to be 1.97 times higher than in those without this infection. The CI for this pooled RR was 1.57–2.47, indicating a significant association with high precision due to the narrow CI (RR: 1.97; 95% CI 1.57–2.47) (Fig.  2 ). The analysis of heterogeneity in this meta-analysis revealed that all the combined studies were homogeneous, with a heterogeneity percentage of 0% and a significance level of 0.81 (I 2 : 0.00%; P: 0.82) (Figs.  2 and 3 ).

The forest plot of effect of H. pylori on the risk of GDM in pregnant women

The Galbraith and Funnel plot of effect of H. pylori on the risk of GDM in pregnant women for determining heterogeneity and publication bias

Publication bias

The funnel plot and Egger’s test were utilized to evaluate publication bias. The results of the funnel plot have been shown in Fig.  3 , indicating the absence of publication bias in the results. The results of the Eggers test were not statistically significant, which indicated the absence of publication bias in the results (B: 0.400; SE: 0.951; P: 0.671) (Fig.  3 ).

Subgroup analyses

Subgroup analyses in this meta-analysis were conducted based on the age of pregnant mothers, gestational age, type of studies, and various H. pylori diagnostic methods. The results, detailed in Table  3 , reveal differences in the impact of H. pylori infection on the occurrence of GDM, depending on the diagnostic method used. Additionally, the analysis showed that the likelihood of H. pylori infection causing GDM decreased as the age of pregnant mothers increased. Specifically, the infection was 2.728 (RR: 2.728; 95% CI 1.390–5.354) times more likely to cause GDM in women under the age of 29, whereas, for women older than 29, the risk was 1.671 (RR: 1.671; 95% CI 1.242–2.248) (Table  3 ). The meta-regression analysis further confirmed this inverse association. However, the association between H. pylori infection and the occurrence of GDM with increasing maternal age was not statistically significant (B: − 0.071; SE: 0.046; P: 0.221; 95% CI − 0.220, 0.076), as shown in Table  3 .

The subgroup analysis focusing on gestational age revealed that the risk of GDM in pregnant women with H. pylori infection increases as the gestational age progresses. Specifically, the analysis, as detailed in Table  3 , indicated that the risk of developing gestational diabetes in women with H. pylori infection was 1.790 (RR: 1.790; 95% CI 1.337–2.396) for those with a gestational age of less than 30 weeks. This risk increased to 1.942 (RR: 1.942; 95% CI 0.918–4.106) in women with a gestational age of more than 30 weeks, as shown in Table  3 . Additionally, the meta-regression analysis supported this direct association between H. pylori infection and an increased risk of GDM with advancing gestational age, although this finding was not statistically significant (B: 0.081; SE: 0.345; P: 0.829; 95% CI − 1.018, 1.181).

The subgroup analysis results indicated that the association between H. pylori infection and the occurrence of GDM, when combining the case–control studies, was 2.122 (RR: 2.122; 95% CI 1.590, 3.064). However, after combining the cohort studies, the effect size was found to be 1.788 (RR: 1.788; 95% CI 1.300, 2.423) (Table  3 ).

This study primarily aimed to explore the link between H. pylori infection and the incidence of GDM in pregnant women. Our findings indicated that pregnant mothers with H. pylori infection were 91% more likely to develop GDM than pregnant mothers without H. pylori infection. This result was derived from combining entirely homogeneous studies, exhibiting no heterogeneity. Furthermore, the calculated CI, ranging from 1.51 to 2.42, signifies the high accuracy of our estimate, as evidenced by the narrowness of this interval. Previous studies have indicated that pregnancy may heighten sensitivity to H. pylori , making expectant mothers more susceptible to this infection. This increased susceptibility is likely due to immunological adaptations during pregnancy, which are essential for the mother’s tolerance of the semi-allogeneic embryo [ 1 , 11 , 19 ].

Generally, pregnancy is known to reduce cellular cytotoxic immune responses in both innate and humoral immunity. This reduction potentially creates favorable conditions for H. pylori infection [ 21 , 22 , 23 ]. Moreover, due to a variety of physiological and immunological changes occurring during pregnancy, there is an increased risk of gastrointestinal infections, with H. pylori being particularly noteworthy [ 24 ]. There is also a possibility that these conditions cause the activation of latent H. pylori infections during pregnancy [ 11 ]. Previous studies have identified several factors contributing to the reduction of IgG levels during pregnancy. These factors include decreased cellular immunity, the excretion of proteins through urine, the hemodilution of IgG transferred from the mother to the fetus via the placenta, and the impact of pregnancy-related hormones, particularly steroid hormones, on protein synthesis [ 25 ].

Since H. pylori infection is most likely acquired before pregnancy, it is widely believed that hormonal and immunological changes during pregnancy can activate latent H. pylori [ 15 , 26 ]. In addition, the decrease in gastric acid production in early pregnancy as a result of increased fluid accumulation in the pregnant mother’s body, steroid hormonal changes, and immune tolerance can lead to the activation of a latent H. pylori infection [ 26 , 27 ]. The link between H. pylori infection and insulin resistance can be attributed to several biological mechanisms. Firstly, changes in glucose metabolism might lead to alterations in the gastric mucosa’s chemical makeup, facilitating the diagnosis of H. pylori infection. Secondly, H. pylori infection in the stomach triggers an increase in pro-inflammatory cytokines, causing structural changes in insulin-binding agents and subsequently hindering their interaction with insulin. These effects may become more pronounced as gestational age increases in mothers, potentially strengthening the link between H. pylori infection and GDM in pregnant women [ 28 , 29 , 30 , 31 ].

The subgroup analysis in our meta-analysis revealed that a gestational age of 30 weeks or more could intensify the association between H. pylori infection and GDM in pregnant women. Additionally, a stronger correlation between H. pylori infection and GDM was observed in younger pregnant mothers, specifically those under 29 years of age. However, this finding should be interpreted with caution due to the limited number of studies in this subgroup, which numbered only two. Consequently, the reliability of these results might be limited, and further research in this area is necessary.

The findings of the subgroup analysis revealed that when data from case–control studies were coupled with cohort studies, the effect size, which reflects the magnitude of the observed treatment impact, increased. This shows that the findings from the case–control studies had a stronger association or treatment impact than the cohort studies. It is crucial to remember that case–control studies have inherent limitations and are prone to bias, particularly in terms of participant selection and data collection on exposure and outcome variables [ 32 , 33 ].

One of the limitations of our study is the small number of studies included in some subgroup analyses. Future research, with a larger number of studies in this field, could facilitate more comprehensive meta-analyses or cohort studies with extensive sample sizes. Although we performed subgroup analyses based on infection diagnosis method, gestational age, and maternal age, the lack of data on key variables like body mass index, history of diabetes or H. pylori infection before pregnancy, and various treatments for diabetes and infection, limited our ability to conduct subgroup analyses on these factors. To assess the impact of these variables and their role in the relationship between H. pylori infection and GDM, designing and implementing large-scale cohort studies is essential.

The results of this meta-analysis unequivocally show that pregnant women with H. pylori infection have a higher chance of developing gestational diabetes. Given these findings, it is imperative that both developed and developed countries create and execute comprehensive healthcare standards to inform and prevent H. pylori infection in expectant mothers. Care strategies that prioritize early detection and adequate treatment of H. pylori infection before, during, and following pregnancy should be part of this. Additionally, considering the high probability of latent H. pylori activation during pregnancy, which can lead to the development of GDM, prompt action for identifying and eliminating this infection before pregnancy is imperative.

Availability of data and materials

Data and materials are available by request to the corresponding author (Dr. Yousef Moradi).

Abbreviations

Confidence interval

Risk ratio/relative risk

• Gestational diabetes mellitus

Newcastle ottawa scale

Preferred reporting items for systematic reviews and meta-analyses

Choudhury AA, Rajeswari VD. Gestational diabetes mellitus-a metabolic and reproductive disorder. Biomed Pharmacother. 2021;143:112183.

Article   PubMed   CAS   Google Scholar  

Kalra P, Kachhwaha CP, Singh HV. Prevalence of gestational diabetes mellitus and its outcome in western Rajasthan. Indian J Endocrinol Metab. 2013;17(4):677.

Article   PubMed   PubMed Central   Google Scholar  

Li J, Fan M, Ma F, Zhang S, Li Q. The effects of Helicobacter pylori infection on pregnancy-related diseases and fetal development in diabetes in pregnancy. Ann Transl Med. 2021;9(8):686.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Damm P. Future risk of diabetes in mother and child after gestational diabetes mellitus. Int J Gynecol Obstet. 2009;104:S25–6.

Article   Google Scholar  

Rac H, Gould AP, Eiland LS, Griffin B, McLaughlin M, Stover KR, et al. Common bacterial and viral infections: review of management in the pregnant patient. Ann Pharmacother. 2019;53(6):639–51.

Article   PubMed   Google Scholar  

Chow AW, Jewesson PJ. Pharmacokinetics and safety of antimicrobial agents during pregnancy. Rev Infect Dis. 1985;7(3):287–313.

Jacob L, Kalder M, Kostev K. Prevalence and predictors of prescription of antibiotics in pregnant women treated by gynecologists in Germany. Int J Clin Pharmacol Ther. 2017;55(8):643.

Zhan Y, Si M, Li M, Jiang Y. The risk of Helicobacter pylori infection for adverse pregnancy outcomes: a systematic review and meta-analysis. Helicobacter. 2019;24(2):e12562.

Li L, Tan J, Liu L, Li J, Chen G, Chen M, et al. Association between H. pylori infection and health outcomes: an umbrella review of systematic reviews and meta-analyses. BMJ Open. 2020;10(1):e031951.

Kuo F-C, Wu C-Y, Kuo C-H, Wu C-F, Lu C-Y, Chen Y-H, et al. The utilization of a new immunochromatographic test in detection of Helicobacter pylori antibody from maternal and umbilical cord serum. BioMed Res Int. 2014;2014:568410.

Xia B, Wang W, Lu Y, Chen C. Helicobacter pylori infection increases the risk of metabolic syndrome in pregnancy: a cohort study. Ann Transl Med. 2020;8(14):875.

Weyermann M, Borowski C, Bode G, Gürbüz B, Adler G, Brenner H, et al. Helicobacter pylori –specific immune response in maternal serum, cord blood, and human milk among mothers with and without current helicobacter pylori infection. Pediatr Res. 2005;58(5):897–902.

Mustafa A, Bilal NE, Abass AE, Elhassan EM, Adam I. The association between Helicobacter pylori seropositivity and low birthweight in a Sudanese maternity hospital. Int J Gynecol Obstet. 2018;143(2):191–4.

Article   CAS   Google Scholar  

Kitagawa M, Natori M, Katoh M, Sugimoto K, Omi H, Akiyama Y, et al. Maternal transmission of Helicobacter pylori in the perinatal period. J Obstet Gynaecol Res. 2001;27(4):225–30.

Cardaropoli S, Rolfo A, Todros T. Helicobacter pylori and pregnancy-related disorders. World J Gastroenterol WJG. 2014;20(3):654.

Alshareef SA, Rayis DA, Adam I, Gasim GI. Helicobacter pylori infection, gestational diabetes mellitus and insulin resistance among pregnant Sudanese women. BMC Res Notes. 2018;11(1):1–5.

Mubarak N, Gasim GI, Khalafalla KE, Ali NI, Adam I. Helicobacter pylori , anemia, iron deficiency and thrombocytopenia among pregnant women at Khartoum, Sudan. Trans R Soc Trop Med Hyg. 2014;108(6):380–4.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group* P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

Cardaropoli S, Giuffrida D, Piazzese A, Todros T. Helicobacter pylori seropositivity and pregnancy-related diseases: a prospective cohort study. J Reprod Immunol. 2015;109:41–7.

Alshareef SA, Rayis DA, Adam I, Gasim GI. Helicobacter pylori infection, gestational diabetes mellitus and insulin resistance among pregnant Sudanese women. BMC Res Notes. 2018;11(1):517.

Ashraf R, Shah NP. Immune system stimulation by probiotic microorganisms. Crit Rev Food Sci Nutr. 2014;54(7):938–56.

Gombart AF, Pierre A, Maggini S. A review of micronutrients and the immune system—working in harmony to reduce the risk of infection. Nutrients. 2020;12(1):236.

Abrams ET, Miller EM. The roles of the immune system in Women’s reproduction: evolutionary constraints and life history trade-offs. Am J Phys Anthropol. 2011;146(S53):134–54.

Al-Rifai RH, Abdo NM, Paulo MS, Saha S, Ahmed LA. Prevalence of gestational diabetes mellitus in the middle east and North Africa, 2000–2019: a systematic review, meta-analysis, and meta-regression. Front Endocrinol. 2021;12:668447.

Tang Y, Yang Y, Lv Z. Adverse pregnancy outcomes and Helicobacter pylori infection: a meta-analysis. Int J Clin Pract. 2021;75(10):e14588.

Mosbah A, Nabiel Y. Helicobacter pylori , Chlamydiae pneumoniae and trachomatis as probable etiological agents of preeclampsia. J Matern Fetal Neonatal Med. 2016;29(10):1607–12.

Ibrahim HA. Relationship between helicobacter pylori infection, serum vitamin D3 level and spontaneous abortion. Int J Gen Med. 2020;13:469.

Macha JM. Prevalence of helicobacter-pylori infection and clinical characteristics of women with low-risk early pregnancy attending hospitals in Dodoma city: The University of Dodoma, Dodoma; 2020.

Wu C-Y, Tseng J-J, Chou M-M, Lin S-K, Poon S-K, Chen G-H. Correlation between Helicobacter pylori infection and gastrointestinal symptoms in pregnancy. Adv Ther. 2000;17(3):152–8.

Ng QX, Venkatanarayanan N, De Deyn MLZQ, Ho CYX, Mo Y, Yeo WS. A meta-analysis of the association between Helicobacter pylori ( H. pylori ) infection and hyperemesis gravidarum. Helicobacter. 2018;23(1):e12455.

Yisak H, Belete D, Mahtsentu Y. Helicobacter pylori infection and related factors among pregnant women at Debre Tabor General Hospital, Northwest Ethiopia, 2021: Anemia highly related with H. pylori . Women’s Health. 2022;18:17455057221092266.

PubMed   PubMed Central   CAS   Google Scholar  

Savitz DA, Wellenius GA, Savitz DA, Wellenius GA. Selection bias in case-control studies. In: Interpreting epidemiologic evidence: connecting research to applications. Oxford University Press; 2016. p. 0.

Lanza A, Ravaud P, Riveros C, Dechartres A. Comparison of estimates between cohort and case-control studies in meta-analyses of therapeutic interventions: a meta-epidemiological study. PLoS ONE. 2016;11(5):e0154877.

Ahmed MA, Hassan NG, Omer ME, Rostami A, Rayis DA, Adam I. Helicobacter pylori and Chlamydia trachomatis in Sudanese women with preeclampsia. J Matern Fetal Neonatal Med. 2020;33(12):2023–6.

Li Na LN, Yang Yue YY. Correlation between Helicobacter pylori infection during pregnancy and pregnancy complication. J Trop Med. 2017:970–972.

Guiqing L, Jun L, Huanling L, Yan X. The relationship between Helicobacter pylori infection and obstetric complications in pregnant women. Prog Obstet Gynecol. 2013;22(5):432.

Download references

Acknowledgements

Not applicable.

Author information

Authors and affiliations.

Student Research Committee, Kurdistan University of Medical Sciences, Sanandaj, Iran

Parisa Kohnepoushi & Rozhin Mansouri

Research Center of Pediatric Infection Diseases, Institute of Immunology and Infection Diseases, Iran University of Medical Sciences, Tehran, Iran

Ali Baradaran Moghaddam

Department of Pharmaceutical and Administrative Sciences, College of Pharmacy, Western New England University, 1215 Wilbraham Road, Springfield, MA, 01119, USA

Marzieh Soheili & Hamed Gilzad Kohan

Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran

Yousef Moradi

You can also search for this author in PubMed   Google Scholar

Contributions

YM identified the review topic and designed the search strategy. PK and MS performed the initial search, and both authors were involved in the screening data extraction, risk of bias assessment and certainty of evidence grading. YM performed the meta-analyses. HGK, RM, HRB, YM, and PK wrote the first draft of the manuscript, and both authors provided substantial intellectual input to the subsequent edits and have read and approved the final manuscript. YM is the guarantor of the work and has primary responsibility for the content presented in this manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Corresponding authors

Correspondence to Marzieh Soheili or Yousef Moradi .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kohnepoushi, P., Mansouri, R., Moghaddam, A.B. et al. The association between the Helicobacter pylori infection and the occurrence of gestational diabetes: a systematic review and meta-analysis. J Health Popul Nutr 43 , 136 (2024). https://doi.org/10.1186/s41043-024-00630-3

Download citation

Received : 18 March 2024

Accepted : 20 August 2024

Published : 31 August 2024

DOI : https://doi.org/10.1186/s41043-024-00630-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Evidence synthesis

Journal of Health, Population and Nutrition

ISSN: 2072-1315

• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 02 September 2024

Association of maternal gut microbial metabolites with gestational diabetes mellitus: evidence from an original case-control study, meta-analysis, and Mendelian randomization

• Mengxin Yao 1 , 2 ,
• Yue Xiao 2 ,
• Yanqun Sun 3 ,
• Bing Zhang 4 ,
• Yaling Ding 2 ,
• Qiuping Ma 5 ,
• Fei Liang 2 ,
• Zhuoqiao Yang 2 ,
• Wenxin Ge 2 ,
• Songliang Liu 5 ,
• Lili Xin 6 ,
• Jieyun Yin   ORCID: orcid.org/0000-0002-5265-3930 2 , 6 &
• Xiaoyan Zhu 1 , 7  

European Journal of Clinical Nutrition ( 2024 ) Cite this article

Metrics details

• Gestational diabetes

The associations of gut microbial metabolites, such as trimethylamine N -oxide (TMAO), its precursors, and phenylacetylglutamine (PAGln), with the risk of gestational diabetes mellitus (GDM) remain unclear.

Serum samples of 201 women with GDM and 201 matched controls were collected and then targeted metabolomics was performed to examine the metabolites of interest. Multivariable conditional logistic regression was applied to investigate the relationship between metabolites and GDM. Meta-analysis was performed to combine our results and four similar articles searched from online databases, and Mendelian randomization (MR) analysis was eventually conducted to explore the causalities.

In the case-control study, after dichotomization and comparing the higher versus the lower group, the adjusted odds ratio and 95% confidence interval of choline and L-carnitine with GDM were 2.124 (1.186–3.803) and 0.293 (0.134–0.638), respectively; but neutral relationships between TMAO, betaine, and PAGln with GDM were observed. The following meta-analysis consistently revealed that L-carnitine was negatively associated with GDM. However, MR analyses showed no evidence of causalities.

Conclusions

Maternal levels of L-carnitine were related to the risk of GDM in both the original case-control study and meta-analysis. However, we did not observe any genetic evidence to establish a causal relationship between this metabolite and GDM.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 print issues and online access

251,40 € per year

only 20,95 € per issue

Buy this article

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Data availability

Data are available from the corresponding author upon reasonable request.

ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and diagnosis of diabetes: standards of care in Diabetes-2023. Diab Care. 2023;46:S19–s40. https://doi.org/10.2337/dc23-S002 .

Article   Google Scholar  

Sweeting A, Wong J, Murphy HR, Ross GP. A clinical update on gestational diabetes mellitus. Endocr Rev. 2022;43:763–93. https://doi.org/10.1210/endrev/bnac003 .

Article   PubMed   PubMed Central   Google Scholar  

Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF diabetes Atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in pregnancy study Group’s criteria. Diab Res Clin Pract. 2022;183:109050 https://doi.org/10.1016/j.diabres.2021.109050 .

Bar-Zeev Y, Haile ZT, Chertok IA. Association between prenatal smoking and gestational diabetes mellitus. Obstet Gynecol. 2020;135:91–99. https://doi.org/10.1097/aog.0000000000003602 .

Article   PubMed   Google Scholar  

Rönö K, Masalin S, Kautiainen H, Gissler M, Raina M, Eriksson JG, et al. Impact of maternal income on the risk of gestational diabetes mellitus in primiparous women. Diabet Med. 2019;36:214–20. https://doi.org/10.1111/dme.13834 .

Schwartz N, Nachum Z, Green MS. The prevalence of gestational diabetes mellitus recurrence–effect of ethnicity and parity: a metaanalysis. Am J Obstet Gynecol. 2015;213:310–7. https://doi.org/10.1016/j.ajog.2015.03.011 .

Li G, Wei T, Ni W, Zhang A, Zhang J, Xing Y, et al. Incidence and risk factors of gestational diabetes mellitus: a prospective cohort study in Qingdao, China. Front Endocrinol. 2020;11:636 https://doi.org/10.3389/fendo.2020.00636 .

Johns EC, Denison FC, Norman JE, Reynolds RM. Gestational diabetes mellitus: mechanisms, treatment, and complications. Trends Endocrinol Metab. 2018;29:743–54. https://doi.org/10.1016/j.tem.2018.09.004 .

Malaza N, Masete M, Adam S, Dias S, Nyawo T, Pheiffer C. A systematic review to compare adverse pregnancy outcomes in women with pregestational diabetes and gestational diabetes. Int J Environ Res Public Health 2022;19:1084; https://doi.org/10.3390/ijerph191710846 .

Buchanan TA, Xiang AH, Page KA. Gestational diabetes mellitus: risks and management during and after pregnancy. Nat Rev Endocrinol. 2012;8:639–49. https://doi.org/10.1038/nrendo.2012.96 .

Canyelles M, Tondo M, Cedó L, Farràs M, Escolà-Gil JC, Blanco-Vaca F. Trimethylamine N-Oxide: a link among diet, gut microbiota, gene regulation of liver and intestine cholesterol homeostasis and HDL function. Int J Mol Sci. 2018;19; https://doi.org/10.3390/ijms19103228 .

Cho CE, Taesuwan S, Malysheva OV, Bender E, Tulchinsky NF, Yan J et al. Trimethylamine-N-oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: a randomized controlled trial. Mol Nutr Food Res. 2017;61; https://doi.org/10.1002/mnfr.201600324 .

Miele L, Giorgio V, Alberelli MA, De Candia E, Gasbarrini A, Grieco A. Impact of gut microbiota on obesity, diabetes, and cardiovascular disease risk. Curr Cardiol Rep. 2015;17:120 https://doi.org/10.1007/s11886-015-0671-z .

Kalagi NA, Thota RN, Stojanovski E, Alburikan KA, Garg ML. Association between plasma Trimethylamine N-Oxide Levels and Type 2 diabetes: a case control study. Nutrients. 2022;14; https://doi.org/10.3390/nu14102093 .

Dambrova M, Latkovskis G, Kuka J, Strele I, Konrade I, Grinberga S, et al. Diabetes is associated with higher Trimethylamine N-oxide plasma levels. Exp Clin Endocrinol Diabetes. 2016;124:251–6. https://doi.org/10.1055/s-0035-1569330 .

Moldave K, Meister A. Synthesis of phenylacetylglutamine by human tissue. J Biol Chem. 1957;229:463–76.

Nemet I, Saha PP, Gupta N, Zhu W, Romano KA, Skye SM, et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell. 2020;180:862–.e822. https://doi.org/10.1016/j.cell.2020.02.016 .

Romano KA, Nemet I, Prasad Saha P, Haghikia A, Li XS, Mohan ML, et al. Gut microbiota-generated phenylacetylglutamine and heart failure. Circ Heart Fail. 2023;16:e009972 https://doi.org/10.1161/circheartfailure.122.009972 .

Fang C, Zuo K, Jiao K, Zhu X, Fu Y, Zhong J et al. PAGln, an atrial fibrillation-linked gut microbial metabolite, acts as a promoter of atrial myocyte injury. Biomolecules. 2022;12: https://doi.org/10.3390/biom12081120 .

Yu F, Li X, Feng X, Wei M, Luo Y, Zhao T, et al. Phenylacetylglutamine, a Novel biomarker in acute ischemic stroke. Front Cardiovasc Med. 2021;8:798765 https://doi.org/10.3389/fcvm.2021.798765 .

Yahaya TO, Salisu T, Abdulrahman YB, Umar AK. Update on the genetic and epigenetic etiology of gestational diabetes mellitus: a review. Egypt J Med Hum Genet. 2020;21:13 https://doi.org/10.1186/s43042-020-00054-8 .

Hauspurg A, Ying W, Hubel CA, Michos ED, Ouyang P. Adverse pregnancy outcomes and future maternal cardiovascular disease. Clin Cardiol. 2018;41:239–46. https://doi.org/10.1002/clc.22887 .

Barzilay E, Moon A, Plumptre L, Masih SP, Sohn KJ, Visentin CE, et al. Fetal one-carbon nutrient concentrations may be affected by gestational diabetes. Nutr Res. 2018;55:57–64. https://doi.org/10.1016/j.nutres.2018.04.010 .

Huo X, Li J, Cao YF, Li SN, Shao P, Leng J, et al. Trimethylamine N-Oxide metabolites in early pregnancy and risk of gestational diabetes: a nested case-control study. J Clin Endocrinol Metab. 2019;104:5529–39. https://doi.org/10.1210/jc.2019-00710 .

Li P, Zhong C, Li S, Sun T, Huang H, Chen X, et al. Plasma concentration of trimethylamine-N-oxide and risk of gestational diabetes mellitus. Am J Clin Nutr. 2018;108:603–10. https://doi.org/10.1093/ajcn/nqy116 .

Spanou L, Dimou A, Kostara CE, Bairaktari E, Anastasiou E, Tsimihodimos V. A study of the metabolic pathways affected by gestational diabetes mellitus: comparison with type 2 diabetes. Diagnostics . 2022;12: https://doi.org/10.3390/diagnostics12112881 .

Diaz SO, Pinto J, Graça G, Duarte IF, Barros AS, Galhano E, et al. Metabolic biomarkers of prenatal disorders: an exploratory NMR metabonomics study of second trimester maternal urine and blood plasma. J Proteome Res. 2011;10 : 3732–42: https://doi.org/10.1021/pr200352m .

Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–82. https://doi.org/10.2337/dc09-1848 .

Mikolajczyk RT, Zhang J, Betran AP, Souza JP, Mori R, Gülmezoglu AM, Merialdi M. A global reference for fetal-weight and birthweight percentiles. Lancet. 2011;377:1855–61. https://doi.org/10.1016/S0140-6736(11)60364-4 .

National regional and worldwide estimates of low birthweight in 2015 with trends from 2000: a systematic analysis The Lancet Global Health 2019;7:e849–60. https://doi.org/10.1016/S2214-109X(18)30565-5 .

Araujo Júnior E, Peixoto AB, Zamarian AC, Elito Júnior J, Tonni G. Macrosomia. Best Pract Res Clin Obstet Gynaecol. 2017 Jan;38:83-96. https://doi.org/10.1016/j.bpobgyn.2016.08.003 .

Fluss R, Faraggi D, Reiser B. Estimation of the Youden Index and its associated cutoff point. Biometrical J Biometrische Z. 2005;47:458–72. https://doi.org/10.1002/bimj.200410135 .

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700 https://doi.org/10.1136/bmj.b2700 .

Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol. 2004;33:30–42. https://doi.org/10.1093/ije/dyh132 .

Rhee EP, Ho JE, Chen MH, Shen D, Cheng S, Larson MG, et al. A genome-wide association study of the human metabolome in a community-based cohort. Cell Metab. 2013;18:130–43. https://doi.org/10.1016/j.cmet.2013.06.013 .

Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50. https://doi.org/10.1038/ng.2982 .

Kuang YS, Lu JH, Li SH, Li JH, Yuan MY, He JR, et al. Connections between the human gut microbiome and gestational diabetes mellitus. GigaScience. 2017;6:1–12. https://doi.org/10.1093/gigascience/gix058 .

Fugmann M, Breier M, Rottenkolber M, Banning F, Ferrari U, Sacco V, et al. The stool microbiota of insulin resistant women with recent gestational diabetes, a high risk group for type 2 diabetes. Sci Rep. 2015;5:13212 https://doi.org/10.1038/srep13212 .

Ye D, Huang J, Wu J, Xie K, Gao X, Yan K, et al. Integrative metagenomic and metabolomic analyses reveal gut microbiota-derived multiple hits connected to development of gestational diabetes mellitus in humans. Gut microbes. 2023;15:2154552 https://doi.org/10.1080/19490976.2022.2154552 .

Chávez-Talavera O, Tailleux A, Lefebvre P, Staels B. Bile acid control of metabolism and inflammation in obesity, type 2 diabetes, dyslipidemia, and nonalcoholic fatty liver disease. Gastroenterology. 2017;152:1679–.e1673. https://doi.org/10.1053/j.gastro.2017.01.055 .

Shapiro H, Kolodziejczyk AA, Halstuch D, Elinav E. Bile acids in glucose metabolism in health and disease. J Exp Med. 2018;215:383–96. https://doi.org/10.1084/jem.20171965 .

Gao Z, Yin J, Zhang J, Ward RE, Martin RJ, Lefevre M, et al. Butyrate improves insulin sensitivity and increases energy expenditure in mice. Diabetes. 2009;58:1509–17. https://doi.org/10.2337/db08-1637 .

Canfora EE, Jocken JW, Blaak EE. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 2015;11:577–91. https://doi.org/10.1038/nrendo.2015.128 .

Janeiro MH, Ramírez MJ, Milagro FI, Martínez JA, Solas M. Implication of Trimethylamine N-Oxide (TMAO) in disease: potential biomarker or new therapeutic target. Nutrients. 2018;10; https://doi.org/10.3390/nu10101398 .

Macpherson ME, Hov JR, Ueland T, Dahl TB, Kummen M, Otterdal K, et al. Gut microbiota-dependent Trimethylamine N-Oxide associates with inflammation in common variable immunodeficiency. Front Immunol. 2020;11:574500 https://doi.org/10.3389/fimmu.2020.574500 .

Heianza Y, Sun D, Li X, DiDonato JA, Bray GA, Sacks FM, et al. Gut microbiota metabolites, amino acid metabolites and improvements in insulin sensitivity and glucose metabolism: the POUNDS Lost trial. Gut. 2019;68:263–70. https://doi.org/10.1136/gutjnl-2018-316155 .

Lanz M, Janeiro MH, Milagro FI, Puerta E, Ludwig IA, Pineda-Lucena A, et al. Trimethylamine N-oxide (TMAO) drives insulin resistance and cognitive deficiencies in a senescence accelerated mouse model. Mech Ageing Dev. 2022;204:111668 https://doi.org/10.1016/j.mad.2022.111668 .

Fennema D, Phillips IR, Shephard EA. Trimethylamine and Trimethylamine N-Oxide, a Flavin-Containing Monooxygenase 3 (FMO3)-mediated host-microbiome metabolic axis implicated in health and disease. Drug Metab Dispos. 2016;44:1839–50. https://doi.org/10.1124/dmd.116.070615 .

Jia J, Dou P, Gao M, Kong X, Li C, Liu Z, et al. Assessment of causal direction between gut microbiota-dependent metabolites and cardiometabolic health: a bidirectional mendelian randomization analysis. Diabetes. 2019;68:1747–55. https://doi.org/10.2337/db19-0153 .

Zeng Q, Zhao M, Wang F, Li Y, Li H, Zheng J, et al. Integrating choline and specific intestinal microbiota to classify type 2 diabetes in adults: a machine learning based metagenomics study. Front Endocrinol. 2022;13:906310 https://doi.org/10.3389/fendo.2022.906310 .

Vidal-Casariego A, Burgos-Peláez R, Martínez-Faedo C, Calvo-Gracia F, Valero-Zanuy M, Luengo-Pérez LM, et al. Metabolic effects of L-carnitine on type 2 diabetes mellitus: systematic review and meta-analysis. Exp Clin Endocrinol Diab. 2013;121:234–8. https://doi.org/10.1055/s-0033-1333688 .

Raubenheimer PJ, Nyirenda MJ, Walker BR. A choline-deficient diet exacerbates fatty liver but attenuates insulin resistance and glucose intolerance in mice fed a high-fat diet. Diabetes. 2006;55:2015–20. https://doi.org/10.2337/db06-0097 .

Kathirvel E, Morgan K, Nandgiri G, Sandoval BC, Caudill MA, Bottiglieri T, et al. Betaine improves nonalcoholic fatty liver and associated hepatic insulin resistance: a potential mechanism for hepatoprotection by betaine. Am J Physiol Gastrointest Liver Physiol. 2010;299:G1068–1077. https://doi.org/10.1152/ajpgi.00249.2010 .

Fenkci SM, Fenkci V, Oztekin O, Rota S, Karagenc N. Serum total L-carnitine levels in non-obese women with polycystic ovary syndrome. Hum Reprod. 2008;23:1602–6. https://doi.org/10.1093/humrep/den109 .

Qi S, Liu L, He S, Wang L, Li J, Sun X. Trimethylamine N-Oxide and related metabolites in the serum and risk of type 2 diabetes in the Chinese population: a case-control study. Diab Metab Syndr Obes. 2023;16:547–55. https://doi.org/10.2147/dmso.S398008 .

Ringseis R, Keller J, Eder K. Role of carnitine in the regulation of glucose homeostasis and insulin sensitivity: evidence from in vivo and in vitro studies with carnitine supplementation and carnitine deficiency. Eur J Nutr. 2012;51:1–18. https://doi.org/10.1007/s00394-011-0284-2 .

Zamani M, Pahlavani N, Nikbaf-Shandiz M, Rasaei N, Ghaffarian-Ensaf R, Asbaghi O, et al. The effects of L-carnitine supplementation on glycemic markers in adults: a systematic review and dose-response meta-analysis. Front Nutr. 2022;9 : 1082097; https://doi.org/10.3389/fnut.2022.1082097 .

Muoio DM, Noland RC, Kovalik JP, Seiler SE, Davies MN, DeBalsi KL, et al. Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab. 2012;15:764–77. https://doi.org/10.1016/j.cmet.2012.04.005 .

Molfino A, Cascino A, Conte C, Ramaccini C, Fanelli Rossi, Laviano F. A. Caloric restriction and L-carnitine administration improves insulin sensitivity in patients with impaired glucose metabolism. J Parenter Enter Nutr. 2010;34:295–9. https://doi.org/10.1177/0148607109353440 .

Ma L, Fu G, Liu R, Zhou F, Dong S, Zhou Y, et al. Phenylacetyl glutamine: a novel biomarker for stroke recurrence warning. BMC Neurol. 2023;23:74 https://doi.org/10.1186/s12883-023-03118-5 .

Hazekawa M, Ono K, Nishinakagawa T, Kawakubo-Yasukochi T, Nakashima M. In vitro anti-inflammatory effects of the phenylbutyric acid metabolite phenylacetyl glutamine. Biol Pharm Bull. 2018;41:961–6. https://doi.org/10.1248/bpb.b17-00799 .

Download references

Acknowledgements

We want to thank the participants and investigators of the Framingham Heart Study, the TwinsUK study, the Kooperative Gesundheitsforschung in der Region Augsburg study, and the FinnGen study.

JY is currently receiving grant from the National Natural Science Fund of China (grant number: 82273635). XZ is currently receiving grant from the Gusu Health Talents Program Training Project in Suzhou (grant number: GSWS2023064).

Author information

Authors and affiliations.

Suzhou Center for Disease Prevention and Control, Suzhou, China

Mengxin Yao & Xiaoyan Zhu

Department of Epidemiology and Health Statistics, Medical College of Soochow University, Suzhou, China

Mengxin Yao, Yue Xiao, Yaling Ding, Fei Liang, Zhuoqiao Yang, Wenxin Ge & Jieyun Yin

Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China

Department of Geriatrics, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, China

Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of Taicang, 58 Changsheng Road, Suzhou, China

Qiuping Ma & Songliang Liu

Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, Medical College of Soochow University, Suzhou, China

Lili Xin & Jieyun Yin

School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Xiaoyan Zhu

You can also search for this author in PubMed   Google Scholar

Contributions

MY: Conceptualization, Software, Validation, Investigation, Writing - Original Draft. YX: Methodology, Investigation, Data Curation, Formal analysis. YS: Writing - Review & Editing. BZ: Writing - Review & Editing, Resources. YD: Investigation, Data Curation, Visualization. QM: Conceptualization, Resources, Supervision. FL: Software, Investigation, Data Curation. ZY: Investigation, Data Curation. WG: Investigation, Data Curation, Visualization. SL: Resources, Supervision. LX: Conceptualization. JY: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. XZ: Resources, Writing - Review & Editing, Supervision, Funding acquisition.

Corresponding authors

Correspondence to Jieyun Yin or Xiaoyan Zhu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethics approval and consent to participate

This original study was approved by the ethics committee of Soochow University and First People’s Hospital of Taicang. Written informed consent was obtained from all the patients.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Table s1, table s2, table s3, table s4, table s5, table s6, table s7, figure s1, figure s2, figure s3, rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Yao, M., Xiao, Y., Sun, Y. et al. Association of maternal gut microbial metabolites with gestational diabetes mellitus: evidence from an original case-control study, meta-analysis, and Mendelian randomization. Eur J Clin Nutr (2024). https://doi.org/10.1038/s41430-024-01502-z

Download citation

Received : 05 December 2023

Revised : 06 July 2024

Accepted : 22 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1038/s41430-024-01502-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review

Affiliations.

• 1 School of Nursing and Midwifery, College of Health Sciences University of Ghana Ghana Legon.
• 2 Faculty of Health Sciences Nelson Mandela University Port Elizabeth South Africa.
• 3 Department of Nursing Science Nelson Mandela University Port Elizabeth South Africa.
• PMID: 31871693
• PMCID: PMC6918019
• DOI: 10.1002/nop2.324

Aims and objectives: An integrative literature review searched for, selected, appraised, extracted and synthesized data from existing available guidelines on the nursing management of gestational diabetes mellitus as no such analysis has been found.

Background: Early screening, diagnosis and management of gestational diabetes mellitus are important to prevent or reduce complications during and postpregnancy for both mother and child. A variety of guidelines exists, which assist nurses and midwives in the screening, diagnosis and management of gestational diabetes mellitus.

Design: An integrative literature review.

Methods: The review was conducted in June 2018 following an extensive search of available guidelines according to an adaptation of the stages reported by Whittemore and Knafl (2005, Journal of Advanced Nursing , 52, 546). Thus, a five-step process was used, namely formulation of the review question, literature search, critical appraisal of guidelines identified, data extraction and data analysis. All relevant guidelines were subsequently appraised for rigour and quality by two independent reviewers using the AGREE II tool. Content analysis was used analysing the extracted data.

Results: Following extraction and analysis of data, two major themes were identified from eighteen ( N = 18) guidelines. These were the need for early screening and diagnosis of gestational diabetes mellitus and for nursing management of gestational diabetes mellitus (during pregnancy, intra- and postpartum management). Various guidelines on the nursing management of gestational diabetes mellitus were found; however, guidelines were not always comprehensive, sometimes differed in their recommended practices and did not consider a variety of contextual barriers to the implementation of the recommendations.

Conclusion: Critically, scrutiny of the guidelines is required, both in terms of the best evidence used in their development and in terms of the feasibility of implementation for its context.

Relevance to clinical practice: This study provides a summary of best practices regarding the diagnosis, screening and nursing management of gestational diabetes mellitus that provide guidance for nurse-midwives on maternal and postpartum follow-up care for women at risk or diagnosed with gestational diabetes mellitus.

Keywords: best practices; diagnosis; gestational diabetes mellitus; guidelines; midwife; nurse; nursing management; screening.

© 2019 The Authors. Nursing Open published by John Wiley & Sons Ltd.

PubMed Disclaimer

Conflict of interest statement

The authors have no conflicts of interest to disclose.

PRISMA flow of studies through…

PRISMA flow of studies through the review (adapted from Moher et al., 2009)

Similar articles

• Nursing management of gestational diabetes mellitus in Ghana: Perspectives of nurse-midwives and women. Mensah GP, van Rooyen DRM, Ten Ham-Baloyi W. Mensah GP, et al. Midwifery. 2019 Apr;71:19-26. doi: 10.1016/j.midw.2019.01.002. Epub 2019 Jan 4. Midwifery. 2019. PMID: 30640135
• Gestational diabetes mellitus and postpartum care practices of nurse-midwives. Ko JY, Dietz PM, Conrey EJ, Rodgers L, Shellhaas C, Farr SL, Robbins CL. Ko JY, et al. J Midwifery Womens Health. 2013 Jan-Feb;58(1):33-40. doi: 10.1111/j.1542-2011.2012.00261.x. Epub 2013 Jan 14. J Midwifery Womens Health. 2013. PMID: 23317376 Free PMC article.
• The experiences of midwives and nurses collaborating to provide birthing care: a systematic review. Macdonald D, Snelgrove-Clarke E, Campbell-Yeo M, Aston M, Helwig M, Baker KA. Macdonald D, et al. JBI Database System Rev Implement Rep. 2015 Nov;13(11):74-127. doi: 10.11124/jbisrir-2015-2444. JBI Database System Rev Implement Rep. 2015. PMID: 26657466 Review.
• Screening for gestational diabetes mellitus based on different risk profiles and settings for improving maternal and infant health. Tieu J, McPhee AJ, Crowther CA, Middleton P, Shepherd E. Tieu J, et al. Cochrane Database Syst Rev. 2017 Aug 3;8(8):CD007222. doi: 10.1002/14651858.CD007222.pub4. Cochrane Database Syst Rev. 2017. PMID: 28771289 Free PMC article. Review.
• Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, Moraleda C, Rogers L, Daniels K, Green P. Crider K, et al. Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217. Cochrane Database Syst Rev. 2022. PMID: 36321557 Free PMC article.
• The Implication of Diabetes-Specialized Nurses in Aiming for the Better Treatment and Management of Patients with Diabetes Mellitus: A Brief Narrative Review. Zhu Y, Zhang H, Xi Y, Zhu H, Lu Y, Luo X, Tang Z, Lei H. Zhu Y, et al. Diabetes Ther. 2024 May;15(5):917-927. doi: 10.1007/s13300-024-01558-x. Epub 2024 Mar 12. Diabetes Ther. 2024. PMID: 38472627 Free PMC article. Review.
• Enhancing nursing documentation in Kazakhstan: assessing utilization and standardization for improving patient care. Sydykova B, Smailova D, Khismetova Z, Brimzhanova M, Baigozhina Z, Hosseini H, Latypova N, Izmailovich M. Sydykova B, et al. Front Public Health. 2023 Nov 23;11:1267809. doi: 10.3389/fpubh.2023.1267809. eCollection 2023. Front Public Health. 2023. PMID: 38074771 Free PMC article.
• Best practice guidelines for professional nurses to provide self-management support to adults with tuberculosis-human immunodeficiency virus coinfection: A scoping review. Tornu E, Jordan P, McCaul M. Tornu E, et al. PLoS One. 2023 Sep 12;18(9):e0291529. doi: 10.1371/journal.pone.0291529. eCollection 2023. PLoS One. 2023. PMID: 37699053 Free PMC article. Review.
• Analysis of Obstetric Clinical Nursing Integrating Situational Teaching Simulation. Xiao S, Fang J, Zhao X, Yang L, Tang H, Wang Y. Xiao S, et al. Comput Math Methods Med. 2022 Jun 21;2022:6843196. doi: 10.1155/2022/6843196. eCollection 2022. Comput Math Methods Med. 2022. Retraction in: Comput Math Methods Med. 2023 Jun 28;2023:9798171. doi: 10.1155/2023/9798171. PMID: 35774302 Free PMC article. Retracted.
• Effect and Significance of High-Quality Nursing on Blood Glucose, Pregnancy Outcome, and Neonatal Complications of Patients with Gestational Diabetes Mellitus. Zhong W, Li C, Liu J, Zhou J, Xiao Z, Li C, Wu H. Zhong W, et al. Comput Math Methods Med. 2022 Apr 26;2022:2426417. doi: 10.1155/2022/2426417. eCollection 2022. Comput Math Methods Med. 2022. Retraction in: Comput Math Methods Med. 2023 Dec 6;2023:9782396. doi: 10.1155/2023/9782396. PMID: 35516455 Free PMC article. Retracted.
• American Association of Clinical Endocrinologists and American College of Endocrinology [AACE/ACE] (2015). Clinical practice guidelines for developing a diabetes mellitus comprehensive care plan. American Association of Clinical Endocrinologists; Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959114/pdf/nihms-803042.pdf - PMC - PubMed
• American Diabetes Association [ADA] (2010). Diagnosis and classification of diabetes mellitus. Diabetes Care, 33(1), 6269.
• American College of Obstetrics and Gynaecology [ACOG] (2018a). Gestational diabetes mellitus. ACOG Gestational diabetes resource overview. Retrieved from https://www.acog.org/Womens-Health/Gestational-Diabetes
• American College of Obstetrics and Gynaecology [ACOG] (2018b). ACOG practice bulletin no. 190: Gestational diabetes mellitus. Obstetrics Gynecology, 131(2), e49–e64. https://orcid.org/10.1097/AOG.0000000000002501 - DOI - PubMed
• American Diabetes Association (2018). Strategies for improving care. Sec. 1. In: Standards of medical care in diabetes. Diabetes Care, 36 (Supplement 1):S6–S12.

Publication types

• Search in MeSH

Related information

Linkout - more resources, full text sources.

• Europe PubMed Central
• PubMed Central
• Genetic Alliance

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Deep Learning Model for Gestational Diabetes Prediction Based on Imbalanced Data and Feature Selection Optimization

• Conference paper
• First Online: 30 August 2024
• Cite this conference paper

• Heba Askr 7 &
• Aboul Ella Hassanien 8 , 9  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 394))

Included in the following conference series:

• International Conference on Business Intelligence and Information Technology

Gestational diabetes mellitus (GDM) is a form of elevated blood sugar which appears in pregnancy. It can happen at any point during pregnancy as well as create problems both for the mother and the child, both before and after birth. Giving GDM patients an early and accurate diagnosis is essential for effective treatment along with disease control as well as achieving the third sustainable development goal. Undiagnosed diabetes can lead to several dangerous conditions such as heart attack and kidney disease. This necessitates the need for learning model improvement in GDM detection and evaluation. Digital health has gained significant traction in recent years with the aim of enhancing care for diabetic pregnant women. This technology has generated an enormous amount of data that could be used to improve the management of this chronic disease. Benefiting from this, artificial intelligence (AI) methods, particularly deep learning (DL) which is a newly developed branch of machine learning (ML), are commonly used and showing good outcomes. In this paper, a Multilayer Perceptron (MLP) model is proposed to determine whether a woman has GDM. Pregnant women with and without diabetes are represented in the dataset under consideration. Imbalanced data of 1012 patients with six major features and a target column with result diabetic or non-diabetic have been analyzed and preprocessed. Feature selection optimization is developed to enhance the performance of the proposed model. The results show that the proposed model significantly improved the prediction accuracy over other related works with promising prediction accuracy which reaches almost to 98%.

Scientific Research School of Egypt (SRSEG), https://egyptscience-srge.com/ .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

https://www.un.org/sustainabledevelopment/health

Larabi-Marie-Sainte, Aburahmah, Almohaini, Saba: Current techniques for diabetes prediction: review and case study. Appl. Sci. 9 (21), 4604 (2019). https://doi.org/10.3390/app9214604

Moko, A., Victor-Ikoh, M.: Existential risk prediction models for diabetes mellitus. Br. J. Comput. Network. Inf. Technol. 5 (1), 143–156 (2022). https://doi.org/10.52589/BJCNIT-PM3CRE7I

Article   Google Scholar  

Choi, J.H., Lee, K.A., Moon, J.H., Chon, S., Kim, D.J., Kim, H.J., Kim, N.H., Seo, J.A., Kim, M.K., Lim, J.H., Song, Y., Yang, Y.S., Kim, J.H., Lee, Y.B., Noh, J., Hur, K.Y., Park, J.S., Rhee, S.Y., Kim, H.J., Kim, H.M., Ko, J.H., Kim, N.H., Kim, C.H., Ahn, J., Oh, T.J., Kim, S.K., Kim, J., Han, E., Jin, S.M., Choi, W.S., Moon, M.K.: Committee of clinical practice guidelines; Korean diabetes association. 2023 clinical practice guidelines for diabetes mellitus of the Korean diabetes association. Diabetes Metab. J. 47 (5), 575–594 (2023)

Google Scholar  

Saravanan, P.G., Diabetes in Pregnancy Working, G. Maternal Medicine Clinical Study, O. Royal College of, and U. K. Gynaecologists: Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 8 (9), 793–800 (2020). https://doi.org/10.1016/S2213-8587(20)30161-3

Landon, M.B., et al.: A multicenter, randomized trial of treatment for mild gestational diabetes. New Engl. J. Med. 361 (14), 1339–1348 (2009). https://doi.org/10.1056/NEJMoa0902430

Lu, H.Y., Ding, X., Hirst, J.E., Yang, Y., Yang, J., Mackillop, L., Clifton, D.: Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes. IEEE Rev. Biomed. Eng. (2023) https://doi.org/10.1109/RBME.2023.3242261 . Epub ahead of print. PMID: 37022834

Abbas El-Hefnawy, N., Abdel Raouf, O., Askr, H.: Dynamic routing optimization algorithm for software defined networking. Comput. Mater. Continua 70 (1), 1349–1362 (2022)

Raouf, O.A., Askr, H.: ACOSDN-Ant colony optimization algorithm for dynamic routing in software defined networking. In: 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 141–148 (2019). https://doi.org/10.1109/ICCES48960.2019.9068162

Askr, H., Abdel-Salam, M., Hassanien, A.E.: Copula entropy-based golden jackal optimization algorithm for high-dimensional feature selection problems. Exp. Syst. Appl. 238 (Part B):121582. ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.121582

Askr, H., Darwish, A., Hassanien, A.E., ChatGPT.: The future of metaverse in the virtual era and physical world: analysis and applications. In: Hassanien, A.E., Darwish, A., Torky, M. (eds.) The Future of Metaverse in the Virtual Era and Physical World. Studies in Big Data, vol 123. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-29132-6_4

Askr, H., Hssanien, A.E., Darwish, A.: Prediction of climate change impact based on air flight CO 2  emissions using machine learning: towards green air flights. In: Hassanien, A.E., Darwish, A. (eds.) The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations. Studies in Big Data, vol. 118. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-22456-0_2

Askr, H., Farag, M.A., Hassanien, A.E., Snášel, V., Farrag, T.A.: Many-objective African vulture optimization algorithm: a novel approach for many-objective problems. PLoS ONE 18 (5), e0284110 (2023). https://doi.org/10.1371/journal.pone.0284110

Askr, H., Elgeldawi, E., Aboul Ella, H. et al.: Deep learning in drug discovery: an integrative review and future challenges. Artif. Intell. Rev.  56 , 5975–6037 (2023). https://doi.org/10.1007/s10462-022-10306-1

Farghaly, H.M., Gomaa, M.M., Elgeldawi, E., et al.: A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines. Sci. Rep. 13 , 9171 (2023). https://doi.org/10.1038/s41598-023-36319-6

Jader, R., Aminifar, S.: Predictive model for diagnosis of gestational diabetes in the kurdistan region by a combination of clustering and classification algorithms: an ensemble approach. In: Applied Computational Intelligence and Soft Computing, vol. 2022, Article ID 9749579, 11 pages (2022). https://doi.org/10.1155/2022/9749579

Mishra, P., Singh, U., Pandey, C.M., Mishra, P., Pandey, G.: Application of student’s t-test, analysis of variance, and covariance. Annals Cardiac Anesthesia 22 (4) (2019)

Popescu, M.-C., Balas, V., Perescu-Popescu, L., Mastorakis, N.: Multilayer perceptron and neural networks. WSEAS Trans. Circ. Syst. (2009)

Bruno, P., Calimeri, F.: Using heatmaps for deep learning based disease classification. In: 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Siena, Italy, 2019, pp. 1–7 (2019). https://doi.org/10.1109/CIBCB.2019.8791493

Prati, R.C., Batista, G.E., Monard, M.C.: Data mining with imbalanced class distributions: concepts and methods. In: Indian International Conference Artificial Intelligence, pp. 359–376 (2009)

Rastgoo, M., Lemaitre, G., Massich, J., Morel, O., Marzani, F., Garcia, R., Meriaudeau, F.: Tackling the problem of data imbalancing for melanoma classification. In: Bioimaging (2016)

Al-Zebari, A., Sengur, A.: Performance comparison of machine learning techniques on diabetes disease detection. In: 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, pp. 2–5, Ankara, Turkey (2019)

Vijayan, V.V., Anjali, C.: Prediction and diagnosis of diabetes mellitus—a machine learning approach. In: IEEE Recent Advances in Intelligent Computational Systems, pp. 122–127, Trivandrum, India, 2016 (2015)

Gnanadass, I.: Prediction of gestational diabetes by machine learning algorithms. IEEE Potentials 39 (6), 32–37 (2020)

Sonar, P., Jaya Malini, K.: Diabetes prediction using different machine learning approaches. In: Proceedings of the 3rd International Conference on Computing Methodologies and Communication, pp. 367–371, Erode, India. (2019)

Han, J., Rodriguez, J.C., Beheshti, M.: Discovering decision tree-based diabetes prediction model. Commun. Comput. Inf. Sci. 30 , 99–109 (2009)

Download references

Author information

Authors and affiliations.

Faculty of Computers and Artificial Intelligence, University of Sadat City, Menoufia, Egypt

Faculty of Computer and Artificial Intelligence, Cairo University, Cairo, Egypt

Aboul Ella Hassanien

College of Business Administration (CBA), Kuwait University, Kuwait, Kuwait

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Heba Askr .

Editor information

Editors and affiliations.

Faculty of Computers and Information, Cairo University, Giza, Egypt

Computer and Information Engineering, Harbin University Of Commerce, Harbin, Heilongjiang, China

Dequan Zheng

School of Computer and Information Engg, Harbin University of Commerce, Harbin, China

Zhijie Zhao

Harbin University of Commerce, HARBIN, China

Zhipeng Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper.

Askr, H., Hassanien, A.E. (2024). Deep Learning Model for Gestational Diabetes Prediction Based on Imbalanced Data and Feature Selection Optimization. In: Hassanien, A.E., Zheng, D., Zhao, Z., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2023. Smart Innovation, Systems and Technologies, vol 394. Springer, Singapore. https://doi.org/10.1007/978-981-97-3980-6_54

Download citation

DOI : https://doi.org/10.1007/978-981-97-3980-6_54

Published : 30 August 2024

Publisher Name : Springer, Singapore

Print ISBN : 978-981-97-3979-0

Online ISBN : 978-981-97-3980-6

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research